Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. At Boeing's Space Mission Systems, we innovate and collaborate to advance connectivity for a better world. We are committed to fostering an environment for every teammate that is welcoming, respectful, and inclusive, with exceptional opportunity for professional growth. Find your future with us. SMS is seeking a Chief Engineer, Artificial Intelligence and Autonomy to lead the establishment, growth, and operational maturation of the enterprise AI function across the full portfolio of SMS programs in Seal Beach or El Segundo, CA. This is a newly created management position responsible for increasing the adoption and integration of AI capabilities throughout SMS engineering programs, manufacturing operations, and flight systems. This Chief Engineer will define and execute the Company's AI strategy across strategic domains including manufacturing intelligence, constellation autonomy, spectrum orchestration, and AI-assisted engineering. The role reports to Engineering VP and Space Missions Systems Chief Engineer, with a dotted-line report to the VP of Program Management and cross-functional authority across Engineering, Flight Operations, and Manufacturing. Position Responsibilities: Enterprise AI Strategy & Program Coordination: Develop and maintain a comprehensive enterprise AI strategy that increases the use of AI across the full portfolio of SMS programs, serving as the primary coordinator between programs and engineering leadership Define and present the enterprise AI roadmap to Segment Leadership, the VP of Program Management, and the executive leadership team, including business case definition, value propositions, technology readiness level (TRL) reduction plans, and program on-ramp strategies for AI-assisted engineering and space edge compute Coordinate between SMS programs and engineering organizations on the adoption and deployment of AI-assisted coding tools and autonomous software agents to accelerate development velocity and reduce engineering cycle times Identify, evaluate, and manage strategic AI partnerships, vendor relationships, and technology acquisitions that accelerate the Company's AI capabilities Mature and facilitate the intake, qualification, and prioritization of AI, analytics, and automation projects across programs, ensuring the right problems are addressed with executive sponsorship Organizational Design & Team Building: Establish the AI & Autonomy function as a new organizational capability from the ground up, defining structure, operating model, and governance Recruit, hire, and lead a cross-functional AI team spanning AI/ML engineers, data engineers, data scientists, MLOps engineers, and domain specialists Build and manage an independent AI budget, compute infrastructure (GPU clusters), tooling, and vendor partnerships AI-Assisted Engineering & Autonomous Agents: Drive the adoption of AI-assisted coding and autonomous agents across SMS engineering programs, enabling engineers to leverage generative AI, code copilots, and agentic workflows for design, analysis, and verification tasks Develop the business strategy, TRL reduction roadmap, and program on-ramp plans for integrating AI into engineering workflows and space edge compute flight systems, from concept through flight qualification and operational deployment Facilitate discovery workshops with engineering and program leaders to define problems, quantify value, and scope AI solutions across the portfolio Factory AI Automation & Manufacturing Intelligence: Lead development and deployment of AI automation for factory processes, including photographic and computer vision-based quality inspections of satellite components and assemblies Implement AI-driven engineering buy-off automation, accelerating verification and validation across integration and test workflows through autonomous review and decision support Deploy autonomous research capabilities enabling AI-assisted document analysis, anomaly flagging, and compliance verification across satellite build records and End Item Data Packages (EIDPs) Deliver a fully integrated Factory Product Assurance Automation Platform supporting satellite production at scale Flight Edge Computing & Machine Learning: Create and own the business strategy, TRL reduction plan, and technology maturation roadmap for AI-assisted engineering of space edge compute systems, flight inference processors, and onboard machine learning architectures-from early concept through flight qualification and operational deployment Define hardware architecture for space edge compute, onboard storage, and flight inference systems, enabling AI-driven data store-and-forward, real-time ML inference, and autonomous decision-making capabilities on operational satellites Develop and deploy ML models for constellation scheduling optimization, including beam allocation, power management, thermal budget optimization, and coverage planning Close the sim-to-reality gap by using on-orbit performance truth data to continuously improve predictive models and feed insights back into factory integration and test procedures Spectrum Orchestration & Autonomous Operations: Develop and deploy AI-driven dynamic interference mapping and avoidance protocols across the constellation's operating spectrum, leveraging physics-informed models validated against on-orbit telemetry Build AI-optimized beam-hopping and resource scheduling systems that dynamically allocate spectrum and power resources based on real-time demand, interference conditions, and orbital geometry Deliver the integrated Spectrum Allocation Orchestration Platform as an operational system managing dynamic spectrum access across the commercial constellation Cross-Functional Integration & Stakeholder Engagement: Operate with cross-functional authority across Engineering, Flight Operations, Manufacturing, and Product to embed AI into existing workflows, coordinating with the VP of Program Management to align AI initiatives with program schedules and milestones Establish standing technical syncs with Engineering, Flight Operations, and Manufacturing leadership to ensure AI initiatives are validated against domain expertise before deployment Lead stakeholder communications covering AI strategies, program status, and value delivery across SMS leadership and program teams Create materials and media to convey AI strategies and product value, including presentations, demonstrations, visual diagrams, and technical briefings Position AI as an amplifier of existing engineering capabilities-not a replacement-ensuring adoption is collaborative and trust-based across the organization Basic Qualifications (Required Skills/Experience): Bachelor of Science degree in Engineering, Engineering Technology (including Manufacturing Technology), Computer Science, Data Science, Mathematics, Physics, Chemistry or non-US equivalent qualifications directly related to the work statement 5+ years of experience building and leading artificial intelligence/machine learning organizations from inception, including hiring, budgeting, infrastructure provisioning, and delivery of production machine learning systems Experience with cross-functional leadership working with different organizations and operations teams Experience developing artificial intelligence/machine learning strategies including business case development, TRL (Technology Readiness Level) assessment, and technology transition planning Preferred Qualifications (Desired Skills/Experience): Active TS/SCI clearance 10+ years of experience collaborating with and influencing senior management and executive leadership across engineering, operations, and program management functions Experience with physics-informed machine learning, custom ML models for hardware systems, and AI deployment in safety-critical or mission-critical environments Experience with satellite communications, phased array antenna systems, digital beamforming, or comparable complex RF/signal processing systems Bachelor's degree or higher in Computer Science, Electrical Engineering, Aerospace Engineering, or a related technical field Excellent stakeholder management skills including the ability to communicate with and influence people from varying backgrounds, including members of senior leadership teams Experience in aerospace and defense satellite programs (constellation management, payload engineering, launch operations) Experience with AI-assisted software development tools, autonomous coding agents, and agentic AI workflows in engineering environments Experience with manufacturing AI including computer vision quality inspection, predictive maintenance, and factory digital twins Background in space edge computing, flight inference systems, onboard ML, and flight computer architectures for space applications, including TRL maturation of compute hardware Experience with ASIC/custom silicon architectures and understanding of how DSP hardware intersects with ML inference workloads . click apply for full job details
04/06/2026
Full time
Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. At Boeing's Space Mission Systems, we innovate and collaborate to advance connectivity for a better world. We are committed to fostering an environment for every teammate that is welcoming, respectful, and inclusive, with exceptional opportunity for professional growth. Find your future with us. SMS is seeking a Chief Engineer, Artificial Intelligence and Autonomy to lead the establishment, growth, and operational maturation of the enterprise AI function across the full portfolio of SMS programs in Seal Beach or El Segundo, CA. This is a newly created management position responsible for increasing the adoption and integration of AI capabilities throughout SMS engineering programs, manufacturing operations, and flight systems. This Chief Engineer will define and execute the Company's AI strategy across strategic domains including manufacturing intelligence, constellation autonomy, spectrum orchestration, and AI-assisted engineering. The role reports to Engineering VP and Space Missions Systems Chief Engineer, with a dotted-line report to the VP of Program Management and cross-functional authority across Engineering, Flight Operations, and Manufacturing. Position Responsibilities: Enterprise AI Strategy & Program Coordination: Develop and maintain a comprehensive enterprise AI strategy that increases the use of AI across the full portfolio of SMS programs, serving as the primary coordinator between programs and engineering leadership Define and present the enterprise AI roadmap to Segment Leadership, the VP of Program Management, and the executive leadership team, including business case definition, value propositions, technology readiness level (TRL) reduction plans, and program on-ramp strategies for AI-assisted engineering and space edge compute Coordinate between SMS programs and engineering organizations on the adoption and deployment of AI-assisted coding tools and autonomous software agents to accelerate development velocity and reduce engineering cycle times Identify, evaluate, and manage strategic AI partnerships, vendor relationships, and technology acquisitions that accelerate the Company's AI capabilities Mature and facilitate the intake, qualification, and prioritization of AI, analytics, and automation projects across programs, ensuring the right problems are addressed with executive sponsorship Organizational Design & Team Building: Establish the AI & Autonomy function as a new organizational capability from the ground up, defining structure, operating model, and governance Recruit, hire, and lead a cross-functional AI team spanning AI/ML engineers, data engineers, data scientists, MLOps engineers, and domain specialists Build and manage an independent AI budget, compute infrastructure (GPU clusters), tooling, and vendor partnerships AI-Assisted Engineering & Autonomous Agents: Drive the adoption of AI-assisted coding and autonomous agents across SMS engineering programs, enabling engineers to leverage generative AI, code copilots, and agentic workflows for design, analysis, and verification tasks Develop the business strategy, TRL reduction roadmap, and program on-ramp plans for integrating AI into engineering workflows and space edge compute flight systems, from concept through flight qualification and operational deployment Facilitate discovery workshops with engineering and program leaders to define problems, quantify value, and scope AI solutions across the portfolio Factory AI Automation & Manufacturing Intelligence: Lead development and deployment of AI automation for factory processes, including photographic and computer vision-based quality inspections of satellite components and assemblies Implement AI-driven engineering buy-off automation, accelerating verification and validation across integration and test workflows through autonomous review and decision support Deploy autonomous research capabilities enabling AI-assisted document analysis, anomaly flagging, and compliance verification across satellite build records and End Item Data Packages (EIDPs) Deliver a fully integrated Factory Product Assurance Automation Platform supporting satellite production at scale Flight Edge Computing & Machine Learning: Create and own the business strategy, TRL reduction plan, and technology maturation roadmap for AI-assisted engineering of space edge compute systems, flight inference processors, and onboard machine learning architectures-from early concept through flight qualification and operational deployment Define hardware architecture for space edge compute, onboard storage, and flight inference systems, enabling AI-driven data store-and-forward, real-time ML inference, and autonomous decision-making capabilities on operational satellites Develop and deploy ML models for constellation scheduling optimization, including beam allocation, power management, thermal budget optimization, and coverage planning Close the sim-to-reality gap by using on-orbit performance truth data to continuously improve predictive models and feed insights back into factory integration and test procedures Spectrum Orchestration & Autonomous Operations: Develop and deploy AI-driven dynamic interference mapping and avoidance protocols across the constellation's operating spectrum, leveraging physics-informed models validated against on-orbit telemetry Build AI-optimized beam-hopping and resource scheduling systems that dynamically allocate spectrum and power resources based on real-time demand, interference conditions, and orbital geometry Deliver the integrated Spectrum Allocation Orchestration Platform as an operational system managing dynamic spectrum access across the commercial constellation Cross-Functional Integration & Stakeholder Engagement: Operate with cross-functional authority across Engineering, Flight Operations, Manufacturing, and Product to embed AI into existing workflows, coordinating with the VP of Program Management to align AI initiatives with program schedules and milestones Establish standing technical syncs with Engineering, Flight Operations, and Manufacturing leadership to ensure AI initiatives are validated against domain expertise before deployment Lead stakeholder communications covering AI strategies, program status, and value delivery across SMS leadership and program teams Create materials and media to convey AI strategies and product value, including presentations, demonstrations, visual diagrams, and technical briefings Position AI as an amplifier of existing engineering capabilities-not a replacement-ensuring adoption is collaborative and trust-based across the organization Basic Qualifications (Required Skills/Experience): Bachelor of Science degree in Engineering, Engineering Technology (including Manufacturing Technology), Computer Science, Data Science, Mathematics, Physics, Chemistry or non-US equivalent qualifications directly related to the work statement 5+ years of experience building and leading artificial intelligence/machine learning organizations from inception, including hiring, budgeting, infrastructure provisioning, and delivery of production machine learning systems Experience with cross-functional leadership working with different organizations and operations teams Experience developing artificial intelligence/machine learning strategies including business case development, TRL (Technology Readiness Level) assessment, and technology transition planning Preferred Qualifications (Desired Skills/Experience): Active TS/SCI clearance 10+ years of experience collaborating with and influencing senior management and executive leadership across engineering, operations, and program management functions Experience with physics-informed machine learning, custom ML models for hardware systems, and AI deployment in safety-critical or mission-critical environments Experience with satellite communications, phased array antenna systems, digital beamforming, or comparable complex RF/signal processing systems Bachelor's degree or higher in Computer Science, Electrical Engineering, Aerospace Engineering, or a related technical field Excellent stakeholder management skills including the ability to communicate with and influence people from varying backgrounds, including members of senior leadership teams Experience in aerospace and defense satellite programs (constellation management, payload engineering, launch operations) Experience with AI-assisted software development tools, autonomous coding agents, and agentic AI workflows in engineering environments Experience with manufacturing AI including computer vision quality inspection, predictive maintenance, and factory digital twins Background in space edge computing, flight inference systems, onboard ML, and flight computer architectures for space applications, including TRL maturation of compute hardware Experience with ASIC/custom silicon architectures and understanding of how DSP hardware intersects with ML inference workloads . click apply for full job details
Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. At Boeing's Space Mission Systems, we innovate and collaborate to advance connectivity for a better world. We are committed to fostering an environment for every teammate that is welcoming, respectful, and inclusive, with exceptional opportunity for professional growth. Find your future with us. SMS is seeking a Chief Engineer, Artificial Intelligence and Autonomy to lead the establishment, growth, and operational maturation of the enterprise AI function across the full portfolio of SMS programs in Seal Beach or El Segundo, CA. This is a newly created management position responsible for increasing the adoption and integration of AI capabilities throughout SMS engineering programs, manufacturing operations, and flight systems. This Chief Engineer will define and execute the Company's AI strategy across strategic domains including manufacturing intelligence, constellation autonomy, spectrum orchestration, and AI-assisted engineering. The role reports to Engineering VP and Space Missions Systems Chief Engineer, with a dotted-line report to the VP of Program Management and cross-functional authority across Engineering, Flight Operations, and Manufacturing. Position Responsibilities: Enterprise AI Strategy & Program Coordination: Develop and maintain a comprehensive enterprise AI strategy that increases the use of AI across the full portfolio of SMS programs, serving as the primary coordinator between programs and engineering leadership Define and present the enterprise AI roadmap to Segment Leadership, the VP of Program Management, and the executive leadership team, including business case definition, value propositions, technology readiness level (TRL) reduction plans, and program on-ramp strategies for AI-assisted engineering and space edge compute Coordinate between SMS programs and engineering organizations on the adoption and deployment of AI-assisted coding tools and autonomous software agents to accelerate development velocity and reduce engineering cycle times Identify, evaluate, and manage strategic AI partnerships, vendor relationships, and technology acquisitions that accelerate the Company's AI capabilities Mature and facilitate the intake, qualification, and prioritization of AI, analytics, and automation projects across programs, ensuring the right problems are addressed with executive sponsorship Organizational Design & Team Building: Establish the AI & Autonomy function as a new organizational capability from the ground up, defining structure, operating model, and governance Recruit, hire, and lead a cross-functional AI team spanning AI/ML engineers, data engineers, data scientists, MLOps engineers, and domain specialists Build and manage an independent AI budget, compute infrastructure (GPU clusters), tooling, and vendor partnerships AI-Assisted Engineering & Autonomous Agents: Drive the adoption of AI-assisted coding and autonomous agents across SMS engineering programs, enabling engineers to leverage generative AI, code copilots, and agentic workflows for design, analysis, and verification tasks Develop the business strategy, TRL reduction roadmap, and program on-ramp plans for integrating AI into engineering workflows and space edge compute flight systems, from concept through flight qualification and operational deployment Facilitate discovery workshops with engineering and program leaders to define problems, quantify value, and scope AI solutions across the portfolio Factory AI Automation & Manufacturing Intelligence: Lead development and deployment of AI automation for factory processes, including photographic and computer vision-based quality inspections of satellite components and assemblies Implement AI-driven engineering buy-off automation, accelerating verification and validation across integration and test workflows through autonomous review and decision support Deploy autonomous research capabilities enabling AI-assisted document analysis, anomaly flagging, and compliance verification across satellite build records and End Item Data Packages (EIDPs) Deliver a fully integrated Factory Product Assurance Automation Platform supporting satellite production at scale Flight Edge Computing & Machine Learning: Create and own the business strategy, TRL reduction plan, and technology maturation roadmap for AI-assisted engineering of space edge compute systems, flight inference processors, and onboard machine learning architectures-from early concept through flight qualification and operational deployment Define hardware architecture for space edge compute, onboard storage, and flight inference systems, enabling AI-driven data store-and-forward, real-time ML inference, and autonomous decision-making capabilities on operational satellites Develop and deploy ML models for constellation scheduling optimization, including beam allocation, power management, thermal budget optimization, and coverage planning Close the sim-to-reality gap by using on-orbit performance truth data to continuously improve predictive models and feed insights back into factory integration and test procedures Spectrum Orchestration & Autonomous Operations: Develop and deploy AI-driven dynamic interference mapping and avoidance protocols across the constellation's operating spectrum, leveraging physics-informed models validated against on-orbit telemetry Build AI-optimized beam-hopping and resource scheduling systems that dynamically allocate spectrum and power resources based on real-time demand, interference conditions, and orbital geometry Deliver the integrated Spectrum Allocation Orchestration Platform as an operational system managing dynamic spectrum access across the commercial constellation Cross-Functional Integration & Stakeholder Engagement: Operate with cross-functional authority across Engineering, Flight Operations, Manufacturing, and Product to embed AI into existing workflows, coordinating with the VP of Program Management to align AI initiatives with program schedules and milestones Establish standing technical syncs with Engineering, Flight Operations, and Manufacturing leadership to ensure AI initiatives are validated against domain expertise before deployment Lead stakeholder communications covering AI strategies, program status, and value delivery across SMS leadership and program teams Create materials and media to convey AI strategies and product value, including presentations, demonstrations, visual diagrams, and technical briefings Position AI as an amplifier of existing engineering capabilities-not a replacement-ensuring adoption is collaborative and trust-based across the organization Basic Qualifications (Required Skills/Experience): Bachelor of Science degree in Engineering, Engineering Technology (including Manufacturing Technology), Computer Science, Data Science, Mathematics, Physics, Chemistry or non-US equivalent qualifications directly related to the work statement 5+ years of experience building and leading artificial intelligence/machine learning organizations from inception, including hiring, budgeting, infrastructure provisioning, and delivery of production machine learning systems Experience with cross-functional leadership working with different organizations and operations teams Experience developing artificial intelligence/machine learning strategies including business case development, TRL (Technology Readiness Level) assessment, and technology transition planning Preferred Qualifications (Desired Skills/Experience): Active TS/SCI clearance 10+ years of experience collaborating with and influencing senior management and executive leadership across engineering, operations, and program management functions Experience with physics-informed machine learning, custom ML models for hardware systems, and AI deployment in safety-critical or mission-critical environments Experience with satellite communications, phased array antenna systems, digital beamforming, or comparable complex RF/signal processing systems Bachelor's degree or higher in Computer Science, Electrical Engineering, Aerospace Engineering, or a related technical field Excellent stakeholder management skills including the ability to communicate with and influence people from varying backgrounds, including members of senior leadership teams Experience in aerospace and defense satellite programs (constellation management, payload engineering, launch operations) Experience with AI-assisted software development tools, autonomous coding agents, and agentic AI workflows in engineering environments Experience with manufacturing AI including computer vision quality inspection, predictive maintenance, and factory digital twins Background in space edge computing, flight inference systems, onboard ML, and flight computer architectures for space applications, including TRL maturation of compute hardware Experience with ASIC/custom silicon architectures and understanding of how DSP hardware intersects with ML inference workloads . click apply for full job details
04/05/2026
Full time
Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. At Boeing's Space Mission Systems, we innovate and collaborate to advance connectivity for a better world. We are committed to fostering an environment for every teammate that is welcoming, respectful, and inclusive, with exceptional opportunity for professional growth. Find your future with us. SMS is seeking a Chief Engineer, Artificial Intelligence and Autonomy to lead the establishment, growth, and operational maturation of the enterprise AI function across the full portfolio of SMS programs in Seal Beach or El Segundo, CA. This is a newly created management position responsible for increasing the adoption and integration of AI capabilities throughout SMS engineering programs, manufacturing operations, and flight systems. This Chief Engineer will define and execute the Company's AI strategy across strategic domains including manufacturing intelligence, constellation autonomy, spectrum orchestration, and AI-assisted engineering. The role reports to Engineering VP and Space Missions Systems Chief Engineer, with a dotted-line report to the VP of Program Management and cross-functional authority across Engineering, Flight Operations, and Manufacturing. Position Responsibilities: Enterprise AI Strategy & Program Coordination: Develop and maintain a comprehensive enterprise AI strategy that increases the use of AI across the full portfolio of SMS programs, serving as the primary coordinator between programs and engineering leadership Define and present the enterprise AI roadmap to Segment Leadership, the VP of Program Management, and the executive leadership team, including business case definition, value propositions, technology readiness level (TRL) reduction plans, and program on-ramp strategies for AI-assisted engineering and space edge compute Coordinate between SMS programs and engineering organizations on the adoption and deployment of AI-assisted coding tools and autonomous software agents to accelerate development velocity and reduce engineering cycle times Identify, evaluate, and manage strategic AI partnerships, vendor relationships, and technology acquisitions that accelerate the Company's AI capabilities Mature and facilitate the intake, qualification, and prioritization of AI, analytics, and automation projects across programs, ensuring the right problems are addressed with executive sponsorship Organizational Design & Team Building: Establish the AI & Autonomy function as a new organizational capability from the ground up, defining structure, operating model, and governance Recruit, hire, and lead a cross-functional AI team spanning AI/ML engineers, data engineers, data scientists, MLOps engineers, and domain specialists Build and manage an independent AI budget, compute infrastructure (GPU clusters), tooling, and vendor partnerships AI-Assisted Engineering & Autonomous Agents: Drive the adoption of AI-assisted coding and autonomous agents across SMS engineering programs, enabling engineers to leverage generative AI, code copilots, and agentic workflows for design, analysis, and verification tasks Develop the business strategy, TRL reduction roadmap, and program on-ramp plans for integrating AI into engineering workflows and space edge compute flight systems, from concept through flight qualification and operational deployment Facilitate discovery workshops with engineering and program leaders to define problems, quantify value, and scope AI solutions across the portfolio Factory AI Automation & Manufacturing Intelligence: Lead development and deployment of AI automation for factory processes, including photographic and computer vision-based quality inspections of satellite components and assemblies Implement AI-driven engineering buy-off automation, accelerating verification and validation across integration and test workflows through autonomous review and decision support Deploy autonomous research capabilities enabling AI-assisted document analysis, anomaly flagging, and compliance verification across satellite build records and End Item Data Packages (EIDPs) Deliver a fully integrated Factory Product Assurance Automation Platform supporting satellite production at scale Flight Edge Computing & Machine Learning: Create and own the business strategy, TRL reduction plan, and technology maturation roadmap for AI-assisted engineering of space edge compute systems, flight inference processors, and onboard machine learning architectures-from early concept through flight qualification and operational deployment Define hardware architecture for space edge compute, onboard storage, and flight inference systems, enabling AI-driven data store-and-forward, real-time ML inference, and autonomous decision-making capabilities on operational satellites Develop and deploy ML models for constellation scheduling optimization, including beam allocation, power management, thermal budget optimization, and coverage planning Close the sim-to-reality gap by using on-orbit performance truth data to continuously improve predictive models and feed insights back into factory integration and test procedures Spectrum Orchestration & Autonomous Operations: Develop and deploy AI-driven dynamic interference mapping and avoidance protocols across the constellation's operating spectrum, leveraging physics-informed models validated against on-orbit telemetry Build AI-optimized beam-hopping and resource scheduling systems that dynamically allocate spectrum and power resources based on real-time demand, interference conditions, and orbital geometry Deliver the integrated Spectrum Allocation Orchestration Platform as an operational system managing dynamic spectrum access across the commercial constellation Cross-Functional Integration & Stakeholder Engagement: Operate with cross-functional authority across Engineering, Flight Operations, Manufacturing, and Product to embed AI into existing workflows, coordinating with the VP of Program Management to align AI initiatives with program schedules and milestones Establish standing technical syncs with Engineering, Flight Operations, and Manufacturing leadership to ensure AI initiatives are validated against domain expertise before deployment Lead stakeholder communications covering AI strategies, program status, and value delivery across SMS leadership and program teams Create materials and media to convey AI strategies and product value, including presentations, demonstrations, visual diagrams, and technical briefings Position AI as an amplifier of existing engineering capabilities-not a replacement-ensuring adoption is collaborative and trust-based across the organization Basic Qualifications (Required Skills/Experience): Bachelor of Science degree in Engineering, Engineering Technology (including Manufacturing Technology), Computer Science, Data Science, Mathematics, Physics, Chemistry or non-US equivalent qualifications directly related to the work statement 5+ years of experience building and leading artificial intelligence/machine learning organizations from inception, including hiring, budgeting, infrastructure provisioning, and delivery of production machine learning systems Experience with cross-functional leadership working with different organizations and operations teams Experience developing artificial intelligence/machine learning strategies including business case development, TRL (Technology Readiness Level) assessment, and technology transition planning Preferred Qualifications (Desired Skills/Experience): Active TS/SCI clearance 10+ years of experience collaborating with and influencing senior management and executive leadership across engineering, operations, and program management functions Experience with physics-informed machine learning, custom ML models for hardware systems, and AI deployment in safety-critical or mission-critical environments Experience with satellite communications, phased array antenna systems, digital beamforming, or comparable complex RF/signal processing systems Bachelor's degree or higher in Computer Science, Electrical Engineering, Aerospace Engineering, or a related technical field Excellent stakeholder management skills including the ability to communicate with and influence people from varying backgrounds, including members of senior leadership teams Experience in aerospace and defense satellite programs (constellation management, payload engineering, launch operations) Experience with AI-assisted software development tools, autonomous coding agents, and agentic AI workflows in engineering environments Experience with manufacturing AI including computer vision quality inspection, predictive maintenance, and factory digital twins Background in space edge computing, flight inference systems, onboard ML, and flight computer architectures for space applications, including TRL maturation of compute hardware Experience with ASIC/custom silicon architectures and understanding of how DSP hardware intersects with ML inference workloads . click apply for full job details
This is a Fulltime position, kindy share your updated resume to the e-mail Balakrishna . I can be reached at JD : Agentic AI Engineer (Full Time/ Contarct) Experience: 1-8 years Location: New York, NY, NJ, PA (Onsite) Only US Citizens Design, Develop and Implement agentic ai application with orchestration and workflows using any framework like Langchain/Langraph/Google ADK, etc. Design, Develop and Implement RAG (Retrieval Augmented Generation) with proper word embeddings, chunks, versions using any vector db and framework Collaborate with data engineering teams to integrate the AI agents and utilize the data from APIs, Databases, Data Warehouses, Data Lakes, Structured/unstructured data through any other data pipelines like message brokers that are hosted in AWS ecosystem like S3, Glue, Athena, Redshift, Lambda, etc. Support proof-of-concepts, pilots and production deployments Implement data and AI Agents traceability, reasoning and observability Participate in technical discussions for any use cases Strong proficiency in Python and SQL scripts Nice to have: Experience in Financial service or Regulated Guidelines.
04/01/2026
Full time
This is a Fulltime position, kindy share your updated resume to the e-mail Balakrishna . I can be reached at JD : Agentic AI Engineer (Full Time/ Contarct) Experience: 1-8 years Location: New York, NY, NJ, PA (Onsite) Only US Citizens Design, Develop and Implement agentic ai application with orchestration and workflows using any framework like Langchain/Langraph/Google ADK, etc. Design, Develop and Implement RAG (Retrieval Augmented Generation) with proper word embeddings, chunks, versions using any vector db and framework Collaborate with data engineering teams to integrate the AI agents and utilize the data from APIs, Databases, Data Warehouses, Data Lakes, Structured/unstructured data through any other data pipelines like message brokers that are hosted in AWS ecosystem like S3, Glue, Athena, Redshift, Lambda, etc. Support proof-of-concepts, pilots and production deployments Implement data and AI Agents traceability, reasoning and observability Participate in technical discussions for any use cases Strong proficiency in Python and SQL scripts Nice to have: Experience in Financial service or Regulated Guidelines.
AI Applications Engineer Business Affairs: University IT (UIT), Redwood City, California, United States Information Technology Services Sep 08, 2025 Post Date 107213 Requisition # Job Purpose Are you an experienced AI/GenAI engineer who loves shipping real systems? Join Stanford's Enterprise Technology team to design, implement, and support AI solutions across university use cases. In this role, you will influence strategic direction, requirements, and architecture for AI driven information systems, incorporating new capabilities (LLMs, RAG, agentic frameworks, MLOps) to improve workflow, efficiency, and decision-making. You may serve as the technical lead for specific AI tracks and interrelated applications. This role blends hands-on engineering with mentorship and thought leadership. You will prototype and productionize-presenting proofs of concept, demoing solutions to stakeholders, and partnering with project managers, technical managers, architects, security, infrastructure, and application teams(ServiceNow, Salesforce, Oracle Financials, etc.) Core Duties: AI/ML System Implementation & Integration: Translate requirements into well-engineered components (pipelines, vector stores, prompt/agent logic, evaluation hooks) and implement them in partnership with the platform/architecture team. Application & Agent Development: Build and maintain LLM-based agents/services that securely call enterprise tools (ServiceNow, Salesforce, Oracle, etc.) using approved APIs and tool-calling frameworks. Create lightweight internal SDKs/utilities where needed. RAG & Search Enablement: Configure and optimize RAG workflows (chunking, embeddings, metadata filters) and integrate with existing search/vector infrastructure-escalating architecture changes to designated architects. MLOps & SDLC Practices: Follow and improve team standards for CI/CD, testing, prompt/model versioning, and observability. Own feature delivery through dev/test/prod, coordinating with release managers. Governance, Security & Compliance: Apply established guardrails (PII redaction, policy checks, access controls). Partner with InfoSec and architects to close gaps; document decisions and risks. Metrics & Reporting: Instrument services with KPIs (latency, cost, accuracy/quality) and build lightweight dashboards. Deep BI/reporting not primary. Documentation & Communication: Write clear technical docs (APIs, workflows, runbooks), user stories, and acceptance criteria. Support and sometimes lead UAT/test activities. Collaboration & Mentorship: Facilitate working sessions with stakeholders; mentor junior engineers through code reviews and pair programming; provide concise updates and risk flags. Education & Experience: Bachelor's degree and eight years of relevant experience or a combination of education and relevant experience. Required Knowledge, Skills, and Abilities: Agent/Agentic Framework Experience: Built and shipped at least one production LLM agent or agentic workflow using frameworks such as LangGraph, LangChain, CrewAI/AutoGen, Google Agent Builder/Vertex AI Agents (or equivalent). Able to explain tool selection, orchestration logic, and post deployment support. Proven Delivery: Implemented 3+ AI/ML projects and 2+ GenAI/LLM projects in production, with operational support (monitoring, tuning, incident response). Projects should serve sizable user populations and demonstrate measurable efficiency gains. Strong understanding of AI/ML concepts (LLMs/transformers and classical ML) and experience designing, developing, testing, and deploying AI-driven applications. Programming Expertise: Python (primary) plus experience with Node.js/Next.js/React/TypeScript and Java; demonstrated ability to quickly learn new tools/frameworks. Experience with cloud AI stacks (e.g., Google Vertex AI, AWS Bedrock, Azure OpenAI) and vector/search technologies (Pinecone, Elastic/OpenSearch, FAISS, Milvus, etc.). Knowledge of data design/architecture, relational and NoSQL databases, and data modeling. Thorough understanding of SDLC, MLOps, and quality control practices. Ability to define/solve logical problems for highly technical applications; strong problem-solving and systematic troubleshooting skills. Excellent communication, listening, negotiation, and conflict resolution skills; ability to bridge functional and technical resources. Desired Knowledge, Skills, and Abilities: MLOps Tooling: MLflow, Kubeflow, Vertex Pipelines, SageMaker Pipelines; LangSmith/PromptLayer/Weights & Biases. Open Source Savvy: Experience working with, customizing, and improving open-source solutions; comfortable contributing fixes/features upstream. Rapid Tech Adoption: Demonstrated ability to pick up a new technology/framework quickly and deliver production value with it. GenAI Frameworks: LangChain, LlamaIndex, DSPy, Haystack, LangGraph, Agent Engine, Google ADK, AWS AgentCore, CrewAI/AutoGen. Security & Governance: Implementing AI guardrails, red-teaming, and policy enforcement frameworks. Enterprise Integrations: ServiceNow, Salesforce, Oracle Financials, or others. UI Development: React/Next.js/Tailwind for internal tools. Prompt engineering at scale: Structured prompts (JSON/function-calling), templates, version control; automated/offline & online evals (rubrics, hallucination/bias checks, A/B tests, golden sets). Parameter efficient fine tuning (LoRA/QLoRA/adapters), supervised instruction tuning; hosting open weight models (Llama/Mistral/Qwen) with vLLM/TGI/Ollama. Safety/guardrails frameworks (Guardrails.ai, NeMo Guardrails, Azure/AWS safety filters) and jailbreak/drift detection. Hybrid search & reranking (BM25+dense, Cohere/Voyage/Jina rerankers), synthetic data generation, provenance/watermarking. Telemetry & governance: prompt/model drift monitoring, policy as code, audit logging, red teaming playbooks. Certifications and Licenses: Required: One of (or equivalent experience with): Google/AWS/Azure ML/AI certifications or strong demonstrable portfolio of production AI systems. Physical Requirements : Constantly perform desk-based computer tasks. Frequently sit, grasp lightly/fine manipulation. Occasionally stand/walk, writing by hand. Rarely use a telephone, lift/carry/push/pull objects that weigh up to 10 pounds. Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources by submitting a contact form . Working Conditions: May work extended hours, evenings, and weekends. Work Standards: Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations. Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned. Subject to and expected to stay in sync with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in Stanford's Administrative Guide, . The expected pay range for this position is $169,728 to $190,000 per annum. Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs. At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website ( ) provides detailed information on Stanford's extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process. Why Stanford is for You: Stanford University has revolutionized the way we live and enrich the world. Supporting this mission is our diverse and dedicated 17,000 staff. We seek talent driven to impact the future of our legacy. Our culture and unique perks empower you with: Freedom to grow. We offer career development programs, tuition reimbursement, or audit a course. Join a TedTalk, film screening, or listen to a renowned author or global leader speak. A caring culture. We provide superb retirement plans, generous time-off, and family care resources. A healthier you. Climb our rock wall, or choose from hundreds of health or fitness classes at our world-class exercise facilities. We also provide excellent health care benefits. Discovery and fun. Stroll through historic sculptures, trails, and museums. Enviable resources. Enjoy free commuter programs, ridesharing incentives, discounts, and more. Redwood City. Our new Stanford Redwood City campus, opened in 2019, will be the workplace for approximately 2 . click apply for full job details
01/14/2026
Full time
AI Applications Engineer Business Affairs: University IT (UIT), Redwood City, California, United States Information Technology Services Sep 08, 2025 Post Date 107213 Requisition # Job Purpose Are you an experienced AI/GenAI engineer who loves shipping real systems? Join Stanford's Enterprise Technology team to design, implement, and support AI solutions across university use cases. In this role, you will influence strategic direction, requirements, and architecture for AI driven information systems, incorporating new capabilities (LLMs, RAG, agentic frameworks, MLOps) to improve workflow, efficiency, and decision-making. You may serve as the technical lead for specific AI tracks and interrelated applications. This role blends hands-on engineering with mentorship and thought leadership. You will prototype and productionize-presenting proofs of concept, demoing solutions to stakeholders, and partnering with project managers, technical managers, architects, security, infrastructure, and application teams(ServiceNow, Salesforce, Oracle Financials, etc.) Core Duties: AI/ML System Implementation & Integration: Translate requirements into well-engineered components (pipelines, vector stores, prompt/agent logic, evaluation hooks) and implement them in partnership with the platform/architecture team. Application & Agent Development: Build and maintain LLM-based agents/services that securely call enterprise tools (ServiceNow, Salesforce, Oracle, etc.) using approved APIs and tool-calling frameworks. Create lightweight internal SDKs/utilities where needed. RAG & Search Enablement: Configure and optimize RAG workflows (chunking, embeddings, metadata filters) and integrate with existing search/vector infrastructure-escalating architecture changes to designated architects. MLOps & SDLC Practices: Follow and improve team standards for CI/CD, testing, prompt/model versioning, and observability. Own feature delivery through dev/test/prod, coordinating with release managers. Governance, Security & Compliance: Apply established guardrails (PII redaction, policy checks, access controls). Partner with InfoSec and architects to close gaps; document decisions and risks. Metrics & Reporting: Instrument services with KPIs (latency, cost, accuracy/quality) and build lightweight dashboards. Deep BI/reporting not primary. Documentation & Communication: Write clear technical docs (APIs, workflows, runbooks), user stories, and acceptance criteria. Support and sometimes lead UAT/test activities. Collaboration & Mentorship: Facilitate working sessions with stakeholders; mentor junior engineers through code reviews and pair programming; provide concise updates and risk flags. Education & Experience: Bachelor's degree and eight years of relevant experience or a combination of education and relevant experience. Required Knowledge, Skills, and Abilities: Agent/Agentic Framework Experience: Built and shipped at least one production LLM agent or agentic workflow using frameworks such as LangGraph, LangChain, CrewAI/AutoGen, Google Agent Builder/Vertex AI Agents (or equivalent). Able to explain tool selection, orchestration logic, and post deployment support. Proven Delivery: Implemented 3+ AI/ML projects and 2+ GenAI/LLM projects in production, with operational support (monitoring, tuning, incident response). Projects should serve sizable user populations and demonstrate measurable efficiency gains. Strong understanding of AI/ML concepts (LLMs/transformers and classical ML) and experience designing, developing, testing, and deploying AI-driven applications. Programming Expertise: Python (primary) plus experience with Node.js/Next.js/React/TypeScript and Java; demonstrated ability to quickly learn new tools/frameworks. Experience with cloud AI stacks (e.g., Google Vertex AI, AWS Bedrock, Azure OpenAI) and vector/search technologies (Pinecone, Elastic/OpenSearch, FAISS, Milvus, etc.). Knowledge of data design/architecture, relational and NoSQL databases, and data modeling. Thorough understanding of SDLC, MLOps, and quality control practices. Ability to define/solve logical problems for highly technical applications; strong problem-solving and systematic troubleshooting skills. Excellent communication, listening, negotiation, and conflict resolution skills; ability to bridge functional and technical resources. Desired Knowledge, Skills, and Abilities: MLOps Tooling: MLflow, Kubeflow, Vertex Pipelines, SageMaker Pipelines; LangSmith/PromptLayer/Weights & Biases. Open Source Savvy: Experience working with, customizing, and improving open-source solutions; comfortable contributing fixes/features upstream. Rapid Tech Adoption: Demonstrated ability to pick up a new technology/framework quickly and deliver production value with it. GenAI Frameworks: LangChain, LlamaIndex, DSPy, Haystack, LangGraph, Agent Engine, Google ADK, AWS AgentCore, CrewAI/AutoGen. Security & Governance: Implementing AI guardrails, red-teaming, and policy enforcement frameworks. Enterprise Integrations: ServiceNow, Salesforce, Oracle Financials, or others. UI Development: React/Next.js/Tailwind for internal tools. Prompt engineering at scale: Structured prompts (JSON/function-calling), templates, version control; automated/offline & online evals (rubrics, hallucination/bias checks, A/B tests, golden sets). Parameter efficient fine tuning (LoRA/QLoRA/adapters), supervised instruction tuning; hosting open weight models (Llama/Mistral/Qwen) with vLLM/TGI/Ollama. Safety/guardrails frameworks (Guardrails.ai, NeMo Guardrails, Azure/AWS safety filters) and jailbreak/drift detection. Hybrid search & reranking (BM25+dense, Cohere/Voyage/Jina rerankers), synthetic data generation, provenance/watermarking. Telemetry & governance: prompt/model drift monitoring, policy as code, audit logging, red teaming playbooks. Certifications and Licenses: Required: One of (or equivalent experience with): Google/AWS/Azure ML/AI certifications or strong demonstrable portfolio of production AI systems. Physical Requirements : Constantly perform desk-based computer tasks. Frequently sit, grasp lightly/fine manipulation. Occasionally stand/walk, writing by hand. Rarely use a telephone, lift/carry/push/pull objects that weigh up to 10 pounds. Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources by submitting a contact form . Working Conditions: May work extended hours, evenings, and weekends. Work Standards: Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations. Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned. Subject to and expected to stay in sync with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in Stanford's Administrative Guide, . The expected pay range for this position is $169,728 to $190,000 per annum. Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs. At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website ( ) provides detailed information on Stanford's extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process. Why Stanford is for You: Stanford University has revolutionized the way we live and enrich the world. Supporting this mission is our diverse and dedicated 17,000 staff. We seek talent driven to impact the future of our legacy. Our culture and unique perks empower you with: Freedom to grow. We offer career development programs, tuition reimbursement, or audit a course. Join a TedTalk, film screening, or listen to a renowned author or global leader speak. A caring culture. We provide superb retirement plans, generous time-off, and family care resources. A healthier you. Climb our rock wall, or choose from hundreds of health or fitness classes at our world-class exercise facilities. We also provide excellent health care benefits. Discovery and fun. Stroll through historic sculptures, trails, and museums. Enviable resources. Enjoy free commuter programs, ridesharing incentives, discounts, and more. Redwood City. Our new Stanford Redwood City campus, opened in 2019, will be the workplace for approximately 2 . click apply for full job details
AI Data Engineer Business Affairs: University IT (UIT), Redwood City, California, United States Information Technology Services Sep 08, 2025 Post Date 107222 Requisition # Job Purpose Are you an experienced AI/GenAI engineer who loves shipping real systems with a passion for working with enterprise data? Join Stanford's Enterprise Technology team to design, implement, and support AI solutions across university use cases. In this role, you will influence strategic direction, requirements, and architecture for AI driven information systems, incorporating new capabilities (LLMs, RAG, agentic frameworks, MLOps) to improve workflow, efficiency, and decision-making. You may serve as the technical lead for specific AI tracks and interrelated applications. This role blends hands-on engineering with mentorship and thought leadership. You will prototype and productionize-presenting proofs of concept, demoing solutions to stakeholders, and partnering with project managers, technical managers, architects, security, infrastructure, and application teams (ServiceNow, Salesforce, Oracle Financials, etc.) Core Duties: AI/ML System Implementation & Integration: Translate requirements into well-engineered components (pipelines, vector stores, prompt/agent logic, evaluation hooks) and implement them in partnership with the platform/architecture team. Data Engineering & EDA: Build and optimize data ingestion, transformation, and quality pipelines. Conduct exploratory data analysis (EDA) to surface patterns, anomalies, and insights that inform AI models and decision-making. Application & Agent Development: Build and maintain LLM-based agents/services that securely call enterprise tools (ServiceNow, Salesforce, Oracle, etc.) using approved APIs and tool-calling frameworks. Create lightweight internal SDKs/utilities where needed. RAG & Search Enablement: Configure and optimize RAG workflows (chunking, embeddings, metadata filters) and integrate with existing search/vector infrastructure-escalating architecture changes to designated architects. MLOps & SDLC Practices: Follow and improve team standards for CI/CD, testing, prompt/model versioning, and observability. Own feature delivery through dev/test/prod, coordinating with release managers. Governance, Security & Compliance: Apply established guardrails (PII redaction, policy checks, access controls). Partner with InfoSec and architects to close gaps; document decisions and risks. Metrics & Reporting: Instrument services with KPIs (latency, cost, accuracy/quality) and build lightweight dashboards. Deep BI/reporting. Documentation & Communication: Write clear technical docs (APIs, workflows, runbooks), user stories, and acceptance criteria. Support and sometimes lead UAT/test activities. Collaboration & Mentorship: Facilitate working sessions with stakeholders; mentor junior engineers through code reviews and pair programming; provide concise updates and risk flags. Education & Experience: Bachelor's degree and eight years of relevant experience or a combination of education and relevant experience. Required Knowledge, Skills, and Abilities: Agent/Agentic Framework Experience: Built and shipped at least one production LLM agent or agentic workflow using frameworks such as LangGraph, LangChain, CrewAI/AutoGen, Google Agent Builder/Vertex AI Agents (or equivalent). Able to explain tool selection, orchestration logic, and post deployment support. Proven Delivery: Implemented 3+ AI/ML projects and 2+ GenAI/LLM projects in production, with operational support (monitoring, tuning, incident response). Projects should serve sizable user populations and demonstrate measurable efficiency gains. Enterprise Data Understanding: Strong knowledge of enterprise systems (ServiceNow, Salesforce, Oracle Financials, etc.) and how to extract, transform, and analyze data from them. Data Engineering & Analysis: Proficiency in building data pipelines, conducting exploratory data analysis (EDA), profiling datasets, and preparing features for ML/AI use cases. Strong understanding of AI/ML concepts (LLMs/transformers and classical ML) and experience designing, developing, testing, and deploying AI-driven applications. Programming Expertise: Python (primary), with experience in SQL and one or more general-purpose languages (Java, Node.js, or TypeScript). Experience with cloud AI stacks (e.g., Google Vertex AI, AWS Bedrock, Azure OpenAI) and vector/search technologies (Pinecone, Elastic/OpenSearch, FAISS, Milvus, etc.). Knowledge of data design/architecture, relational and NoSQL databases, and data modeling. Thorough understanding of SDLC, MLOps, and quality control practices. Ability to define/solve logical problems for highly technical applications; strong problem-solving and systematic troubleshooting skills. Excellent communication, listening, negotiation, and conflict resolution skills; ability to bridge functional and technical resources. Desired Knowledge, Skills, and Abilities: MLOps Tooling: MLflow, Kubeflow, Vertex Pipelines, SageMaker Pipelines; LangSmith/PromptLayer/Weights & Biases. Open Source Savvy: Experience working with, customizing, and improving open-source solutions; comfortable contributing fixes/features upstream. Rapid Tech Adoption: Demonstrated ability to pick up a new technology/framework quickly and deliver production value with it. GenAI Frameworks: LangChain, LlamaIndex, DSPy, Haystack, LangGraph, Agent Engine, Google ADK, AWS AgentCore, and CrewAI/AutoGen Security & Governance: Implementing AI guardrails, red-teaming, and policy enforcement frameworks. Enterprise Integrations: ServiceNow, Salesforce, Oracle Financials, or others. UI Development: React/Next.js/Tailwind for internal tools. Prompt engineering at scale: Structured prompts (JSON/function-calling), templates, version control; automated/offline & online evals (rubrics, hallucination/bias checks, A/B tests, golden sets). Parameter efficient fine tuning (LoRA/QLoRA/adapters), supervised instruction tuning; hosting open weight models (Llama/Mistral/Qwen) with vLLM/TGI/Ollama. Safety/guardrails frameworks (Guardrails.ai, NeMo Guardrails, Azure/AWS safety filters) and jailbreak/drift detection. Telemetry & governance: prompt/model drift monitoring, policy as code, audit logging, red teaming playbooks. Advanced Data Techniques: Hybrid search/reranking, synthetic data generation, provenance/watermarking, dataset drift detection. Certifications and Licenses: Required: One or more certifications in Google, AWS, or Azure AI/ML, or equivalent demonstrable portfolio of production AI/data systems. Physical Requirements : Constantly perform desk-based computer tasks. Frequently sit, grasp lightly/fine manipulation. Occasionally stand/walk, writing by hand. Rarely use a telephone, lift/carry/push/pull objects that weigh up to 10 pounds. Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of the job. Working Conditions: May work extended hours, evenings, and weekends. Work Standards: Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations. Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned. Subject to and expected to stay in sync with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in Stanford's Administrative Guide, . The expected pay range for this position is $169,728 to $190,000 per annum. Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs. At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website ( ) provides detailed information on Stanford's extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process. Why Stanford is for You: Stanford University has revolutionized the way we live and enrich the world. Supporting this mission is our diverse and dedicated 17,000 staff. We seek talent driven to impact the future of our legacy. Our culture and unique perks empower you with: Freedom to grow. We offer career development programs, tuition reimbursement, or audit a course. Join a TedTalk, film screening, or listen to a renowned author or global leader speak. A caring culture. We provide superb retirement plans, generous time-off, and family care resources. A healthier you. Climb our rock wall, or choose from hundreds of health or fitness classes at our world-class exercise facilities. We also provide excellent health care benefits. . click apply for full job details
01/14/2026
Full time
AI Data Engineer Business Affairs: University IT (UIT), Redwood City, California, United States Information Technology Services Sep 08, 2025 Post Date 107222 Requisition # Job Purpose Are you an experienced AI/GenAI engineer who loves shipping real systems with a passion for working with enterprise data? Join Stanford's Enterprise Technology team to design, implement, and support AI solutions across university use cases. In this role, you will influence strategic direction, requirements, and architecture for AI driven information systems, incorporating new capabilities (LLMs, RAG, agentic frameworks, MLOps) to improve workflow, efficiency, and decision-making. You may serve as the technical lead for specific AI tracks and interrelated applications. This role blends hands-on engineering with mentorship and thought leadership. You will prototype and productionize-presenting proofs of concept, demoing solutions to stakeholders, and partnering with project managers, technical managers, architects, security, infrastructure, and application teams (ServiceNow, Salesforce, Oracle Financials, etc.) Core Duties: AI/ML System Implementation & Integration: Translate requirements into well-engineered components (pipelines, vector stores, prompt/agent logic, evaluation hooks) and implement them in partnership with the platform/architecture team. Data Engineering & EDA: Build and optimize data ingestion, transformation, and quality pipelines. Conduct exploratory data analysis (EDA) to surface patterns, anomalies, and insights that inform AI models and decision-making. Application & Agent Development: Build and maintain LLM-based agents/services that securely call enterprise tools (ServiceNow, Salesforce, Oracle, etc.) using approved APIs and tool-calling frameworks. Create lightweight internal SDKs/utilities where needed. RAG & Search Enablement: Configure and optimize RAG workflows (chunking, embeddings, metadata filters) and integrate with existing search/vector infrastructure-escalating architecture changes to designated architects. MLOps & SDLC Practices: Follow and improve team standards for CI/CD, testing, prompt/model versioning, and observability. Own feature delivery through dev/test/prod, coordinating with release managers. Governance, Security & Compliance: Apply established guardrails (PII redaction, policy checks, access controls). Partner with InfoSec and architects to close gaps; document decisions and risks. Metrics & Reporting: Instrument services with KPIs (latency, cost, accuracy/quality) and build lightweight dashboards. Deep BI/reporting. Documentation & Communication: Write clear technical docs (APIs, workflows, runbooks), user stories, and acceptance criteria. Support and sometimes lead UAT/test activities. Collaboration & Mentorship: Facilitate working sessions with stakeholders; mentor junior engineers through code reviews and pair programming; provide concise updates and risk flags. Education & Experience: Bachelor's degree and eight years of relevant experience or a combination of education and relevant experience. Required Knowledge, Skills, and Abilities: Agent/Agentic Framework Experience: Built and shipped at least one production LLM agent or agentic workflow using frameworks such as LangGraph, LangChain, CrewAI/AutoGen, Google Agent Builder/Vertex AI Agents (or equivalent). Able to explain tool selection, orchestration logic, and post deployment support. Proven Delivery: Implemented 3+ AI/ML projects and 2+ GenAI/LLM projects in production, with operational support (monitoring, tuning, incident response). Projects should serve sizable user populations and demonstrate measurable efficiency gains. Enterprise Data Understanding: Strong knowledge of enterprise systems (ServiceNow, Salesforce, Oracle Financials, etc.) and how to extract, transform, and analyze data from them. Data Engineering & Analysis: Proficiency in building data pipelines, conducting exploratory data analysis (EDA), profiling datasets, and preparing features for ML/AI use cases. Strong understanding of AI/ML concepts (LLMs/transformers and classical ML) and experience designing, developing, testing, and deploying AI-driven applications. Programming Expertise: Python (primary), with experience in SQL and one or more general-purpose languages (Java, Node.js, or TypeScript). Experience with cloud AI stacks (e.g., Google Vertex AI, AWS Bedrock, Azure OpenAI) and vector/search technologies (Pinecone, Elastic/OpenSearch, FAISS, Milvus, etc.). Knowledge of data design/architecture, relational and NoSQL databases, and data modeling. Thorough understanding of SDLC, MLOps, and quality control practices. Ability to define/solve logical problems for highly technical applications; strong problem-solving and systematic troubleshooting skills. Excellent communication, listening, negotiation, and conflict resolution skills; ability to bridge functional and technical resources. Desired Knowledge, Skills, and Abilities: MLOps Tooling: MLflow, Kubeflow, Vertex Pipelines, SageMaker Pipelines; LangSmith/PromptLayer/Weights & Biases. Open Source Savvy: Experience working with, customizing, and improving open-source solutions; comfortable contributing fixes/features upstream. Rapid Tech Adoption: Demonstrated ability to pick up a new technology/framework quickly and deliver production value with it. GenAI Frameworks: LangChain, LlamaIndex, DSPy, Haystack, LangGraph, Agent Engine, Google ADK, AWS AgentCore, and CrewAI/AutoGen Security & Governance: Implementing AI guardrails, red-teaming, and policy enforcement frameworks. Enterprise Integrations: ServiceNow, Salesforce, Oracle Financials, or others. UI Development: React/Next.js/Tailwind for internal tools. Prompt engineering at scale: Structured prompts (JSON/function-calling), templates, version control; automated/offline & online evals (rubrics, hallucination/bias checks, A/B tests, golden sets). Parameter efficient fine tuning (LoRA/QLoRA/adapters), supervised instruction tuning; hosting open weight models (Llama/Mistral/Qwen) with vLLM/TGI/Ollama. Safety/guardrails frameworks (Guardrails.ai, NeMo Guardrails, Azure/AWS safety filters) and jailbreak/drift detection. Telemetry & governance: prompt/model drift monitoring, policy as code, audit logging, red teaming playbooks. Advanced Data Techniques: Hybrid search/reranking, synthetic data generation, provenance/watermarking, dataset drift detection. Certifications and Licenses: Required: One or more certifications in Google, AWS, or Azure AI/ML, or equivalent demonstrable portfolio of production AI/data systems. Physical Requirements : Constantly perform desk-based computer tasks. Frequently sit, grasp lightly/fine manipulation. Occasionally stand/walk, writing by hand. Rarely use a telephone, lift/carry/push/pull objects that weigh up to 10 pounds. Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of the job. Working Conditions: May work extended hours, evenings, and weekends. Work Standards: Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations. Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned. Subject to and expected to stay in sync with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in Stanford's Administrative Guide, . The expected pay range for this position is $169,728 to $190,000 per annum. Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs. At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website ( ) provides detailed information on Stanford's extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process. Why Stanford is for You: Stanford University has revolutionized the way we live and enrich the world. Supporting this mission is our diverse and dedicated 17,000 staff. We seek talent driven to impact the future of our legacy. Our culture and unique perks empower you with: Freedom to grow. We offer career development programs, tuition reimbursement, or audit a course. Join a TedTalk, film screening, or listen to a renowned author or global leader speak. A caring culture. We provide superb retirement plans, generous time-off, and family care resources. A healthier you. Climb our rock wall, or choose from hundreds of health or fitness classes at our world-class exercise facilities. We also provide excellent health care benefits. . click apply for full job details
Senior Manager, Machine Learning Engineer (People Leader) Capital One's Digital Commerce & Innovation organization is seeking a Machine Learning Engineer, People Leader, at the Senior Manager level with a passion for building and growing full stack applications to join its Velocity Black by Capital One team. As a candidate for this role, you're able to seamlessly switch from diving deep into technology with engineers to driving high-level, strategic discussions. You are a naturally curious technologist and stay on top of emerging trends, including prototyping of nascent technologies. You are not afraid to question any existing processes and solutions, yet you display a keen sense of business value proposition and focus on the right priorities. At Capital One, you will help leverage the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Velocity Black by Capital One harnesses the power of artificial intelligence, the warmth of human experts and the convenience of the latest interfaces to help high-performance people actualize the full potential of their lives. By harnessing 24/7 chat, AI, and mobile payments, we help our customers do more and be more in the digital age. From access to the hottest restaurants to guaranteed upgrades at the world's finest resorts. Make a custom request through the app and you will be chatting to our team within 1 minute, 24/7/365. The service we offer and build upon is unlike anything ever built before, and our product is rapidly evolving. Velocity Black is our core product, built in React Native and offering exclusive access and unbeatable word-wide service across Travel, Experiences, Luxury Goods and Dining. Our internal and bespoke request management platform, Gravity, is built with React and Node.js micro services, virtually augmenting our expert customer service agents with AI, delivering the unrivalled personal service our members expect and love. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. Retrain, maintain, and monitor models in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. Use programming languages like Python, Scala, or Java. Basic Qualifications: Bachelor's degree At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) At least 4 years of experience programming with Python, Scala, or Java At least 3 years of experience building, scaling, and optimizing ML systems At least 2 years of experience leading teams developing ML solutions At least 4 years of people management experience. Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 3+ years of experience developing performant, resilient, and maintainable code 3+ years of experience with data gathering and preparation for ML models Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform 3+ years of experience building production-ready data pipelines that feed ML models Ability to communicate complex technical concepts clearly to a variety of audiences ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. New York, NY: $245,900 - $280,600 for Sr. Mgr, Machine Learning Engineering Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
12/17/2025
Full time
Senior Manager, Machine Learning Engineer (People Leader) Capital One's Digital Commerce & Innovation organization is seeking a Machine Learning Engineer, People Leader, at the Senior Manager level with a passion for building and growing full stack applications to join its Velocity Black by Capital One team. As a candidate for this role, you're able to seamlessly switch from diving deep into technology with engineers to driving high-level, strategic discussions. You are a naturally curious technologist and stay on top of emerging trends, including prototyping of nascent technologies. You are not afraid to question any existing processes and solutions, yet you display a keen sense of business value proposition and focus on the right priorities. At Capital One, you will help leverage the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Velocity Black by Capital One harnesses the power of artificial intelligence, the warmth of human experts and the convenience of the latest interfaces to help high-performance people actualize the full potential of their lives. By harnessing 24/7 chat, AI, and mobile payments, we help our customers do more and be more in the digital age. From access to the hottest restaurants to guaranteed upgrades at the world's finest resorts. Make a custom request through the app and you will be chatting to our team within 1 minute, 24/7/365. The service we offer and build upon is unlike anything ever built before, and our product is rapidly evolving. Velocity Black is our core product, built in React Native and offering exclusive access and unbeatable word-wide service across Travel, Experiences, Luxury Goods and Dining. Our internal and bespoke request management platform, Gravity, is built with React and Node.js micro services, virtually augmenting our expert customer service agents with AI, delivering the unrivalled personal service our members expect and love. What you'll do in the role: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation). Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications. Retrain, maintain, and monitor models in production. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models. Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code. Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI. Use programming languages like Python, Scala, or Java. Basic Qualifications: Bachelor's degree At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply) At least 4 years of experience programming with Python, Scala, or Java At least 3 years of experience building, scaling, and optimizing ML systems At least 2 years of experience leading teams developing ML solutions At least 4 years of people management experience. Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow 3+ years of experience developing performant, resilient, and maintainable code 3+ years of experience with data gathering and preparation for ML models Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform 3+ years of experience building production-ready data pipelines that feed ML models Ability to communicate complex technical concepts clearly to a variety of audiences ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. New York, NY: $245,900 - $280,600 for Sr. Mgr, Machine Learning Engineering Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Mandatory Qualifications: Strong proficiency in Python Expertise in FastAPI for backend development Experience with LangChain and LangGraph Hands-on experience with Retrieval-Augmented Generation (RAG) Proficiency in vector databases (Milvus, FAISS, or similar) Database integration (MongoDB/PostgreSQL, GraphDB) Experience integrating AWS Bedrock models Desired Qualifications: Prompt engineering for optimizing LLM interactions Ability to design and deploy AI agents using LangGraph, CrewAI etc Experience in multi-step reasoning and AI workflows Experience deploying AI models in production environments Expertise in AWS cloud platforms for scalable Generative AI applications Familiarity with data science libraries (scikit-learn, TensorFlow) Equal Opportunity Employer We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.
12/17/2025
Mandatory Qualifications: Strong proficiency in Python Expertise in FastAPI for backend development Experience with LangChain and LangGraph Hands-on experience with Retrieval-Augmented Generation (RAG) Proficiency in vector databases (Milvus, FAISS, or similar) Database integration (MongoDB/PostgreSQL, GraphDB) Experience integrating AWS Bedrock models Desired Qualifications: Prompt engineering for optimizing LLM interactions Ability to design and deploy AI agents using LangGraph, CrewAI etc Experience in multi-step reasoning and AI workflows Experience deploying AI models in production environments Expertise in AWS cloud platforms for scalable Generative AI applications Familiarity with data science libraries (scikit-learn, TensorFlow) Equal Opportunity Employer We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.
Job Title: GenAI Senior Developer - AWS Bedrock Role Type: Contract Duration: 6 Months Location: Dallas, TX (Onsite) Experience: 7+ Years Skill Category: Digital Cloud DevOps Job Summary We are seeking a highly skilled GenAI Senior Developer with strong Cloud DevOps expertise to design, build, and deploy enterprise-grade Generative AI solutions onAWS Bedrock. The ideal candidate will have hands-on experience withLLM-based applications, cloud-native architectures, and DevOps automation, ensuring scalable, secure, and reliable AI platforms. Key Responsibilities Design, develop, and deploy Generative AI applications using AWS Bedrock and foundation models. Build and optimize LLM pipelines, including prompt engineering, inference workflows, and model integrations. Implement Retrieval-Augmented Generation (RAG) solutions using vector databases and semantic search. Develop backend services using Python and/or NodeJS for AI-driven applications. Automate cloud infrastructure using Terraform / CloudFormation following Infrastructure-as-Code (IaC) best practices. Build and manage CI/CD pipelines for AI and cloud workloads using Git-based workflows. Deploy and manage workloads on AWS services such as Lambda, ECS, EKS, EC2, S3, DynamoDB, API Gateway, and SQS. Ensure high availability, scalability, and performance of GenAI platforms. Implement monitoring, logging, and observability using CloudWatch and APM tools. Apply security best practices including IAM roles, encryption, secrets management, and compliance controls. Collaborate with data scientists, ML engineers, and DevOps teams to productionize AI models. Troubleshoot production issues and optimize system reliability using SRE principles. Required Skills 7+ years of experience in Cloud DevOps / Software Engineering. Strong expertise in AWS Cloud and cloud-native architectures. Hands-on experience with AWS Bedrock and Generative AI solutions. Proficiency in Python (preferred) and/or NodeJS. Experience with LLMs, prompt engineering, RAG, vector databases (Pinecone, FAISS, OpenSearch, etc.). Strong experience with Terraform and Infrastructure-as-Code. CI/CD experience using GitLab, GitHub, or Jenkins. Experience with Docker and Kubernetes (EKS/ECS). Strong understanding of Linux, networking, and cloud security. Excellent troubleshooting and problem-solving skills. Preferred Qualifications Experience with LangChain, Agents, Agentic AI frameworks. Knowledge of MLOps practices and model lifecycle management. Experience with APM tools like Dynatrace, Datadog, or New Relic. Prior experience deploying enterprise-scale GenAI platforms. Strong communication skills and ability to work onsite with cross-functional teams.
12/17/2025
Job Title: GenAI Senior Developer - AWS Bedrock Role Type: Contract Duration: 6 Months Location: Dallas, TX (Onsite) Experience: 7+ Years Skill Category: Digital Cloud DevOps Job Summary We are seeking a highly skilled GenAI Senior Developer with strong Cloud DevOps expertise to design, build, and deploy enterprise-grade Generative AI solutions onAWS Bedrock. The ideal candidate will have hands-on experience withLLM-based applications, cloud-native architectures, and DevOps automation, ensuring scalable, secure, and reliable AI platforms. Key Responsibilities Design, develop, and deploy Generative AI applications using AWS Bedrock and foundation models. Build and optimize LLM pipelines, including prompt engineering, inference workflows, and model integrations. Implement Retrieval-Augmented Generation (RAG) solutions using vector databases and semantic search. Develop backend services using Python and/or NodeJS for AI-driven applications. Automate cloud infrastructure using Terraform / CloudFormation following Infrastructure-as-Code (IaC) best practices. Build and manage CI/CD pipelines for AI and cloud workloads using Git-based workflows. Deploy and manage workloads on AWS services such as Lambda, ECS, EKS, EC2, S3, DynamoDB, API Gateway, and SQS. Ensure high availability, scalability, and performance of GenAI platforms. Implement monitoring, logging, and observability using CloudWatch and APM tools. Apply security best practices including IAM roles, encryption, secrets management, and compliance controls. Collaborate with data scientists, ML engineers, and DevOps teams to productionize AI models. Troubleshoot production issues and optimize system reliability using SRE principles. Required Skills 7+ years of experience in Cloud DevOps / Software Engineering. Strong expertise in AWS Cloud and cloud-native architectures. Hands-on experience with AWS Bedrock and Generative AI solutions. Proficiency in Python (preferred) and/or NodeJS. Experience with LLMs, prompt engineering, RAG, vector databases (Pinecone, FAISS, OpenSearch, etc.). Strong experience with Terraform and Infrastructure-as-Code. CI/CD experience using GitLab, GitHub, or Jenkins. Experience with Docker and Kubernetes (EKS/ECS). Strong understanding of Linux, networking, and cloud security. Excellent troubleshooting and problem-solving skills. Preferred Qualifications Experience with LangChain, Agents, Agentic AI frameworks. Knowledge of MLOps practices and model lifecycle management. Experience with APM tools like Dynatrace, Datadog, or New Relic. Prior experience deploying enterprise-scale GenAI platforms. Strong communication skills and ability to work onsite with cross-functional teams.
