6 jobs found

Job Description Job Description Company Description About AbbVie AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at . on LinkedIn, Facebook, Instagram, X and YouTube. Job Description The Principal Data Scientist is the senior technical and scientific leader for AbbVie's omnichannel engagement analytics, a data and analytics-driven engagement optimization program spanning channel mix, content, field representative call planning, and orchestration. Operating within the APEX Enterprise Advanced Analytics and Innovation team, this role sets the multi-year science vision for personalized commercial engagement and is accountable for the technical strategy, methodology, and scientific rigor of the AI/ML and statistical solutions that power it. Reporting to the Associate Director, Data Science, the Principal is a thought partner to commercial and analytics leadership: shaping which problems are worth solving, forming and defending a point of view on how AI creates measurable business value, and partnering with product owners to turn that vision into scalable solutions. The role combines deep, hands-on expertise in applied machine learning, generative AI, causal inference, and experimentation with the consultative skill to align a complex stakeholder ecosystem around the right answer. Key Responsibilities Define the Data Science vision: Own the multi-year Data Science vision and technical roadmap for omnichannel engagement analytics, anticipating where both the business and the field of AI are heading. Shape how AI creates business value: identify, size, and prioritize opportunities where AI can drive measurable commercial impact, build the business case, and influence senior leadership decisions on where to invest. Lead hands-on with Gen-AI: Personally use AI-assisted development and agentic tools to prototype, test hypotheses, and demonstrate value quickly; build LLM-backed systems using advanced RAG and context engineering, paired with rigorous scientific evaluation frameworks to scale highly reliable omnichannel recommendations. Lead the design of predictive and causal models: Develop next-best-action, recommendation, and sequential decision models that inform customer engagement and resource allocation using complex, multi-source data. Own measurement science: set the standard for measuring effectiveness, incrementality, and business impact using experimental and quasi-experimental methods, so that AI investments are evaluated rigorously. Partner with product owners to shape problems, not just fulfill requirements: co-define business outcomes, scope, and success metrics, and translate them into scalable technical solutions. Navigate a complex stakeholder ecosystem: build alignment across brand and marketing leaders, field sales and operations, Medical, Legal and Regulatory (MLR), Compliance, BTS, Digital Lab, and external vendors; reframe requests into well-posed problems and resolve competing priorities. Communicate with executive impact: translate complex methods and findings into clear recommendations and compelling narratives for executive and non-technical audiences. Productionize at scale: Partner with BTS, Digital Lab, engineering, and platform teams to deploy, monitor, and manage the lifecycle of AI/ML solutions. Raise the bar for scientific rigor: establish best practices in model development, code quality, reproducibility, peer review, and responsible AI across the data science community. Be a thought leader: stay at the forefront of research and practice in machine learning, generative AI, and causal inference; bring new ideas into the team; provoke rigorous analytical debate; and represent AbbVie's point of view internally and in the broader analytics community. Supervisory / Management Responsibilities This role does not carry formal direct report responsibilities but is expected to act as a technical leader and mentor across the broader data science community, raising the bar on scientific rigor and hands-on AI practice. The Principal Data Scientist may lead technical workstreams with external analytics vendors and partners, including oversight of deliverable quality and methodology validation. Key Competencies Scientific Engineering Depth: Deep expertise in machine learning, causal inference, and generative AI, combined with strong engineering discipline in reproducibility, deployment, and enterprise integration. AI Fluency and Bias to Action: Uses AI hands-on in daily work to prototype, analyze, and build; critically evaluates AI output; and turns ideas into working solutions quickly. Business Judgment and Problem-Framing: Translates ambiguous business challenges into well-scoped problems, connects technical choices to commercial value, and sets long-term technical direction while staying close to delivery. Stakeholder Navigation and Executive Influence: Reads organizational dynamics to build alignment across commercial, regulatory, and technology partners, earning trust as a thought partner to win support from senior leaders. Thought Leadership: Challenges conventional thinking and sparks meaningful analytical debate with peers and executives. Qualifications Education Bachelor's Degree in Statistics, Mathematics, Computer Science, Engineering, or another quantitative discipline required; Master's or PhD in a quantitative field strongly preferred. Required Experience 8+ years of experience in data science, machine learning, or applied science (or 6+ years with a PhD), with a track record of delivering production-quality models that drove measurable business impact. Demonstrated experience setting technical direction for a portfolio of AI/ML solutions and influencing decisions of senior business leaders. Experience building personalization, recommendation, decision-optimization, or marketing-science solutions, in any industry. Deep hands-on expertise in causal inference and experimentation, including A/B testing and quasi-experimental methods, to measure business impact. Demonstrated hands-on experience using generative AI and AI-assisted development tools (e.g., LLM APIs, AI coding assistants, agent frameworks) to prototype and deliver solutions, with concrete examples that can be walked through during the interview process. Expert proficiency in Python and SQL, with experience in deep learning frameworks (e.g., PyTorch, TensorFlow) and distributed data processing. Experience productionizing AI/ML solutions in cloud environments, including deployment, monitoring, and lifecycle management. Track record of leading initiatives across complex, cross-functional stakeholder environments and presenting to executive audiences. Preferred Experience Commercial analytics experience in pharmaceutical, biotech, healthcare, or life sciences, including familiarity with data sources such as IQVIA, Symphony Health, APLD, or similar Rx, claims, and engagement data. Experience in omnichannel analytics, such as journey analysis, touchpoint measurement, closed-loop measurement, or next-best-action for HCP or patient engagement. Experience with advanced ML and optimization methods, including reinforcement learning, multi-armed bandits, transformers, and other approaches for sequential or recommendation use cases. Experience building LLM-based and agentic AI systems, including orchestration frameworks, evaluation, and integration through APIs and pipelines. Experience with cloud ML platforms and MLOps tools such as Azure ML, Databricks, and MLflow. External visibility through conference talks, publications, patents, open-source contributions, or recognized industry thought leadership. Experience working in agile product development environments. Additional Information Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future. We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees. This job is eligible to participate in our long-term incentive programs. Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company's sole and absolute discretion, consistent with applicable law. AbbVie is an equal opportunity employer and is committed to operating with integrity . click apply for full job details

Job Description Job Description Company Description About AbbVie AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at . on LinkedIn, Facebook, Instagram, X and YouTube. Job Description The Principal Data Scientist is the senior technical and scientific leader for AbbVie's omnichannel engagement analytics, a data and analytics-driven engagement optimization program spanning channel mix, content, field representative call planning, and orchestration. Operating within the APEX Enterprise Advanced Analytics and Innovation team, this role sets the multi-year science vision for personalized commercial engagement and is accountable for the technical strategy, methodology, and scientific rigor of the AI/ML and statistical solutions that power it. Reporting to the Associate Director, Data Science, the Principal is a thought partner to commercial and analytics leadership: shaping which problems are worth solving, forming and defending a point of view on how AI creates measurable business value, and partnering with product owners to turn that vision into scalable solutions. The role combines deep, hands-on expertise in applied machine learning, generative AI, causal inference, and experimentation with the consultative skill to align a complex stakeholder ecosystem around the right answer. Key Responsibilities Define the Data Science vision: Own the multi-year Data Science vision and technical roadmap for omnichannel engagement analytics, anticipating where both the business and the field of AI are heading. Shape how AI creates business value: identify, size, and prioritize opportunities where AI can drive measurable commercial impact, build the business case, and influence senior leadership decisions on where to invest. Lead hands-on with Gen-AI: Personally use AI-assisted development and agentic tools to prototype, test hypotheses, and demonstrate value quickly; build LLM-backed systems using advanced RAG and context engineering, paired with rigorous scientific evaluation frameworks to scale highly reliable omnichannel recommendations. Lead the design of predictive and causal models: Develop next-best-action, recommendation, and sequential decision models that inform customer engagement and resource allocation using complex, multi-source data. Own measurement science: set the standard for measuring effectiveness, incrementality, and business impact using experimental and quasi-experimental methods, so that AI investments are evaluated rigorously. Partner with product owners to shape problems, not just fulfill requirements: co-define business outcomes, scope, and success metrics, and translate them into scalable technical solutions. Navigate a complex stakeholder ecosystem: build alignment across brand and marketing leaders, field sales and operations, Medical, Legal and Regulatory (MLR), Compliance, BTS, Digital Lab, and external vendors; reframe requests into well-posed problems and resolve competing priorities. Communicate with executive impact: translate complex methods and findings into clear recommendations and compelling narratives for executive and non-technical audiences. Productionize at scale: Partner with BTS, Digital Lab, engineering, and platform teams to deploy, monitor, and manage the lifecycle of AI/ML solutions. Raise the bar for scientific rigor: establish best practices in model development, code quality, reproducibility, peer review, and responsible AI across the data science community. Be a thought leader: stay at the forefront of research and practice in machine learning, generative AI, and causal inference; bring new ideas into the team; provoke rigorous analytical debate; and represent AbbVie's point of view internally and in the broader analytics community. Supervisory / Management Responsibilities This role does not carry formal direct report responsibilities but is expected to act as a technical leader and mentor across the broader data science community, raising the bar on scientific rigor and hands-on AI practice. The Principal Data Scientist may lead technical workstreams with external analytics vendors and partners, including oversight of deliverable quality and methodology validation. Key Competencies Scientific Engineering Depth: Deep expertise in machine learning, causal inference, and generative AI, combined with strong engineering discipline in reproducibility, deployment, and enterprise integration. AI Fluency and Bias to Action: Uses AI hands-on in daily work to prototype, analyze, and build; critically evaluates AI output; and turns ideas into working solutions quickly. Business Judgment and Problem-Framing: Translates ambiguous business challenges into well-scoped problems, connects technical choices to commercial value, and sets long-term technical direction while staying close to delivery. Stakeholder Navigation and Executive Influence: Reads organizational dynamics to build alignment across commercial, regulatory, and technology partners, earning trust as a thought partner to win support from senior leaders. Thought Leadership: Challenges conventional thinking and sparks meaningful analytical debate with peers and executives. Qualifications Education Bachelor's Degree in Statistics, Mathematics, Computer Science, Engineering, or another quantitative discipline required; Master's or PhD in a quantitative field strongly preferred. Required Experience 8+ years of experience in data science, machine learning, or applied science (or 6+ years with a PhD), with a track record of delivering production-quality models that drove measurable business impact. Demonstrated experience setting technical direction for a portfolio of AI/ML solutions and influencing decisions of senior business leaders. Experience building personalization, recommendation, decision-optimization, or marketing-science solutions, in any industry. Deep hands-on expertise in causal inference and experimentation, including A/B testing and quasi-experimental methods, to measure business impact. Demonstrated hands-on experience using generative AI and AI-assisted development tools (e.g., LLM APIs, AI coding assistants, agent frameworks) to prototype and deliver solutions, with concrete examples that can be walked through during the interview process. Expert proficiency in Python and SQL, with experience in deep learning frameworks (e.g., PyTorch, TensorFlow) and distributed data processing. Experience productionizing AI/ML solutions in cloud environments, including deployment, monitoring, and lifecycle management. Track record of leading initiatives across complex, cross-functional stakeholder environments and presenting to executive audiences. Preferred Experience Commercial analytics experience in pharmaceutical, biotech, healthcare, or life sciences, including familiarity with data sources such as IQVIA, Symphony Health, APLD, or similar Rx, claims, and engagement data. Experience in omnichannel analytics, such as journey analysis, touchpoint measurement, closed-loop measurement, or next-best-action for HCP or patient engagement. Experience with advanced ML and optimization methods, including reinforcement learning, multi-armed bandits, transformers, and other approaches for sequential or recommendation use cases. Experience building LLM-based and agentic AI systems, including orchestration frameworks, evaluation, and integration through APIs and pipelines. Experience with cloud ML platforms and MLOps tools such as Azure ML, Databricks, and MLflow. External visibility through conference talks, publications, patents, open-source contributions, or recognized industry thought leadership. Experience working in agile product development environments. Additional Information Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future. We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees. This job is eligible to participate in our long-term incentive programs. Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company's sole and absolute discretion, consistent with applicable law. AbbVie is an equal opportunity employer and is committed to operating with integrity . click apply for full job details

Job Description Job Description Software Guidance & Assistance, Inc., (SGA), is searching for a Machine Learning Engineer for a contract assignment with one of our premier BANKING clients in Cincinnati, OH . Responsibilities : Design, build, and optimize scalable machine learning platforms and production ML systems. Focus on enabling end-to-end machine learning lifecycle management, including feature engineering, model development, deployment automation, monitoring, governance, and continuous improvement. ML Platform Engineering & Model Operations Design, develop, deploy, and manage production-grade machine learning solutions. Build scalable and reusable machine learning platforms that support model development, training, validation, deployment, and monitoring. Implement best practices for model versioning, experimentation tracking, governance, and reproducibility. Develop frameworks to support both batch and real-time inference workloads. Optimize machine learning systems for performance, reliability, maintainability, and cost efficiency. MLOps Pipeline Development Design and maintain automated CI/CD/CT (Continuous Integration, Continuous Delivery, Continuous Training) pipelines for machine learning workflows. Automate model testing, validation, deployment, rollback, and retraining processes. Establish infrastructure-as-code practices and automated environment provisioning. Build operational tooling to support model lifecycle management across development, testing, and production environments. Feature Store & Data Engineering Integration Design and support enterprise feature stores to improve feature reuse, consistency, and model governance. Collaborate with data engineering teams to integrate machine learning solutions with data warehouses, data lakes, and operational data sources. Develop scalable data pipelines supporting both training and inference workloads. Optimize data availability, quality, lineage, and observability. Production Monitoring & Reliability Implement model monitoring frameworks covering: Model performance Data quality Data drift Concept drift Feature drift System health and reliability Establish alerting, observability, and incident management processes. Develop strategies for model retraining and continuous performance improvement. Real-Time Machine Learning Systems Build and support low-latency scoring and prediction platforms. Develop event-driven machine learning architectures using streaming technologies. Partner with business and engineering teams to enable near real-time decisioning capabilities. Governance, Security & Compliance Implement MLOps governance standards and controls. Ensure compliance with enterprise security, privacy, risk, and regulatory requirements. Support model auditability, explainability, lineage tracking, and documentation. Partner with risk, compliance, and cybersecurity teams to maintain operational standards. Collaboration & Technical Leadership Collaborate closely with data scientists, analytics teams, product owners, and software engineers. Translate business requirements into scalable technical solutions. Mentor junior engineers and contribute to MLOps standards, patterns, and best practices. Drive adoption of modern machine learning engineering methodologies across the organization. The engineer will help establish and mature enterprise MLOps capabilities, including: Feature Store implementation and adoption. Model Registry and governance processes. Automated CI/CD/CT pipelines. Real-time model deployment and scoring. Enterprise machine learning monitoring and observability. Reusable machine learning platform services that accelerate model development and deployment across data science teams. Required Skills : 5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering. 3+ years of experience building and supporting production machine learning systems. The ideal candidate has strong experience building and operationalizing machine learning solutions in cloud environments, developing CI/CD pipelines for ML workloads, and collaborating with data scientists, data engineers, software engineers, and business stakeholders. Experience working in highly regulated industries such as financial services, banking, healthcare, insurance, or telecommunications is highly desirable. Demonstrated experience implementing end-to-end machine learning lifecycle management. Experience operating ML workloads in cloud environments (AWS, Azure, or GCP). Experience supporting both batch and real-time machine learning solutions. Experience working with large-scale structured and unstructured data. Machine Learning & MLOps ML lifecycle management Model deployment and orchestration Experiment tracking and model versioning Model monitoring and observability Feature Store architecture Model Registry implementation Automated retraining workflows Responsible AI and model governance practices Cloud Platforms Experience with one or more: AWS SageMaker Azure Machine Learning Google Vertex AI Programming Proficiency in: Python (required) SQL (required) Java, Scala, or R (preferred) Data Platforms Experience with: Snowflake Databricks IBM DB2 or equivalent enterprise RDBMS platforms Data lakes and modern cloud storage platforms Data Transformation Experience with: dbt Spark Pandas PySpark DevOps & Infrastructure Experience with: Git/GitHub/GitLab Jenkins, GitHub Actions, Azure DevOps, or equivalent CI/CD tools Terraform or infrastructure-as-code frameworks Linux administration Shell scripting Containers & Orchestration Experience with: Docker Kubernetes OpenShift (preferred) Streaming & Real-Time Technologies Experience with: Apache Kafka Event-driven architectures Real-time inference pipelines Monitoring & Observability Experience with: Prometheus Grafana ELK Stack Cloud-native monitoring solutions Preferred Skills : Experience implementing enterprise Feature Stores. Experience designing Model Registry and ML Governance frameworks. Experience building real-time decisioning or scoring platforms. Experience supporting credit risk, fraud, customer analytics, recommendation systems, or predictive analytics use cases. Experience with MLFlow, Kubeflow, Airflow, Argo Workflows, Feast, SageMaker Pipelines, or similar MLOps platforms. Knowledge of software architecture patterns and distributed systems. Familiarity with AI/ML regulatory frameworks and model risk management practices. Relevant cloud certifications (AWS, Azure, GCP) preferred. By applying for a job with SGA, you agree to allow SGA to process your application for this and future opportunities in accordance with our Privacy Policy. Also, to ensure timely processing, you agree to be contacted by our AI recruiter via email, text, or phone. Message frequency varies and data rates may apply, but you can reply STOP to any SMS message to opt-out of texts and may contact SGA at to opt-out of AI communications. The choice not to engage with AI will not adversely impact your consideration for placement. AI is not used to make any hiring determinations. SGA is a technology and resource solutions provider driven to stand out. We are a women-owned business. Our mission: to solve big IT problems with a more personal, boutique approach. Each year, we match consultants like you to more than 1,000 engagements. When we say let's work better together, we mean it. You'll join a diverse team built on these core values: customer service, employee development, and quality and integrity in everything we do. Be yourself, love what you do and find your passion at work. Please find us at SGA is an Equal Opportunity Employer and does not discriminate on the basis of Race, Color, Sex, Sexual Orientation, Gender Identity, Religion, National Origin, Disability, Veteran Status, Age, Marital Status, Pregnancy, Genetic Information, or Other Legally Protected Status. We are committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, and our services, programs, and activities. Please visit our company EEO page to request an accommodation or assistance regarding our policy.

Job Description Job Description Responsibilities: Define the technical roadmap and architecture for enterprise AI/ML initiatives aligned with business objectives. Partner with executive leaders across Access, Defense, Fire & Emergency, and Commercial segments to identify high-value analytics opportunities. Champion best practices for MLOps, model governance, ethical AI, and scalable data products. Lead the end-to-end design, development, and deployment of complex predictive, prescriptive, and generative AI models. Translate complex business challenges into production-ready data science frameworks Provide technical mentorship and guidance to senior, mid-level, and associate data scientists and machine learning engineers. Collaborate closely with data engineering, cloud infrastructure, and software teams to ensure robust model deployment and monitoring. Communicate complex analytical concepts and ROI metrics clearly to technical and non-technical stakeholders. Requirements: Experience with industrial IoT, telematics, edge AI, or time-series sensor data from heavy machinery, automotive, or aerospace domains. Background in computer vision (CV), autonomous vehicle tech, or robotics. 8 years of progressive levels of responsibility in analytics and AI in any business organization Programming: Advanced proficiency in Python and SQL; experience with R, Scala, or C is a plus. ML/AI Frameworks: Deep experience with Scikit-learn, PyTorch, TensorFlow, XGBoost, and Hugging Face. Big Data & Cloud: Strong hands-on experience with cloud ecosystems (Azure or AWS), Databricks, Snowflake, and Apache Spark. MLOps & DevOps: Demonstrated expertise in Docker, CI/CD pipelines, MLflow, model registry, monitoring, and automated retraining workflows. Familiarity with Agile/Scrum delivery frameworks within large enterprise environments. 8 years of experience is the manager's expectation for the principal level. Proven experience delivering analytics or AI projects tied to business outcomes, with demonstrated impact on revenue growth or margin improvement. Strong business or domain expertise in manufacturing, supply chain, operations, sales, or lifecycle services. From fields such as automotive, energy, CPG, or healthcare may also fit if they bring relevant business acumen. Experience working between business teams and IT/analytics: engaging senior business leaders (AVP level and above), gathering requirements, translating them into technology problems, and explaining solutions back to the business. Strong communication, business acumen, and ability to influence, including handling challenging conversations with senior leaders. Demonstrated experience mentoring junior team members. Industry Experience: Experience working in a portfolio company with multi-business units; preferably a manufacturing company Bring domain expertise in Supply chain, Manufacturing or Lifecycle services Consulting (nice to have) Personal Characteristics: Exceptional problem-solving, strategic thinking, and architectural design skills. Strong business acumen with the ability to link algorithmic output to measurable industrial impact Fit with Client culture AI and Business savvy person who understands how businesses make money and how AI tools can help businesses solve real business problems Strong EQ with the demonstrated ability to relate to both data scientists and business leaders A strong listener with strong written, and verbal communication skills Change agent who can lead via inspiration and motivation, subject knowledge, and demonstrating a "roll up your sleeves approach to working Preferred: Bachelor's degree in data science, mathematics, economics, statistics, engineering, or information management Advanced Degree (MBA and/or PhD) Background in computer vision (CV), autonomous vehicle tech, or robotics. Familiarity with Agile/Scrum delivery frameworks Awareness of and openness to market-based or vendor-led AI solutions - not assuming every solution must be built in-house. A consultative, outcome-focused approach: able to deliver a solution by building it, partnering with a vendor or consultancy, or using managed services. The Principal Data Scientist serves as the technical authority and strategic lead for enterprise artificial intelligence, machine learning, and advanced analytics initiatives across Client Corporation. In this role, you will architect cutting-edge AI solutions to optimize smart manufacturing, predictive maintenance, aftermarket parts and sales and supply chain operations. We offer attractive, competitive compensation and benefits including, medical, dental, 401k, short-term disability, AD&D, tuition reimbursement, and more. If you take pride in your work and are committed to personal and professional success, let's talk. Please visit to learn more. Zobility is RGBSI's workforce management and staffing division. RGBSI is a multi-national corporation headquartered in Troy, MI with branches throughout the USA, Canada, Germany, and India.

Job Description Job Description Overview We're looking for a Senior Principal AI Engineer to provide hands-on technical leadership across complex, production-grade AI systems. This role is for a builder-architect-someone who has repeatedly taken AI systems from idea to production, understands where they fail at scale, and knows how to unblock teams to move faster without sacrificing outcomes. This is not a pure strategy or people-management role. It is a deeply technical builder role with organizational impact. You will define technical direction by writing code, designing systems, and helping the organization stay in builder mode as complexity and scale increase. What You'll Do Architect and build end-to-end AI systems including LLM orchestration, retrieval layers, agentic workflows, and structured reasoning systems Lead the design of multi-agent and tool-calling systems that operate reliably in production Establish and evolve architecture patterns for scalable, cost-aware, and observable AI applications Drive technical decisions across data modeling, AI pipelines, infrastructure, and APIs Define best practices for evaluation, monitoring, and governance of AI systems in production Mentor senior engineers through design reviews, code reviews, and system-level debugging Translate ambiguous business and domain problems into clear technical strategies Stay ahead of emerging AI techniques and integrate what matters-without chasing hype Core Skills & Experience 12+ years of software engineering experience, with deep hands-on experience building AI/ML systems in production Strong proficiency in TypeScript, React, Go, Python and modern AI frameworks Extensive experience with LLMs, including RAG, tool use, prompt systems, and agentic architectures Proven ability to design and ship large-scale AI systems that run reliably in real-world environments Strong architectural judgment across data systems, AI models, infrastructure, and application layers Deep understanding of AI failure modes: hallucination, drift, brittleness, latency, and cost blowups Excellent communication skills-able to explain technical tradeoffs to both technical and non-technical audiences Track record of shipping systems end-to-end, not just prototypes or research work Builder Mentality (This Is Core to the Role) We are explicitly looking for builders. By " builder, " we mean an operating mode, not a title. Builders: Bias toward systems that solve user needs, not perfect abstractions Move comfortably from ambiguity first draft iteration production Optimize for learning velocity and customer impact, not theoretical completeness Are willing to build the entire arc of a system to surface real constraints early Treat quality as something you earn through iteration, not something you gate progress with Understand that the last 10-20% of a system-integration, edge cases, UX, usability, reliability-is where real work happens At the Principal level, being a builder also means: Helping the organization stay in builder mode as it grows Collapsing unnecessary complexity rather than introducing more process Knowing when architectural rigor matters, and when it is premature Pulling promising work across the finish line instead of waiting for "perfect readiness" Modeling speed, ownership, and clarity for other senior engineers Your impact is measured not only by what you build, but by how much faster and more effectively others can build because of you. Preferred Experience (Domain-Flexible Specialties) Knowledge graph architecture, ontology design, or semantic modeling in complex domains Graph databases, graph query languages, or graph ML techniques Hybrid systems combining structured reasoning with LLM-based approaches Entity resolution, schema alignment, or knowledge fusion at scale AI systems requiring explainability, auditability, or lineage tracking Experience building AI systems in regulated or high-stakes domains (finance, healthcare, legal, government) MLOps, evaluation infrastructure, or long-running AI services operating at scale What You'll Love Owning the technical direction of real AI systems that make it into production Solving hard, ambiguous problems where architecture and execution matter equally Leading through hands-on building, not layers of process Working in an environment that values shipping, learning, and iteration over perfection Having the latitude to shape both systems and how teams build them About Us We are an AI-first company, and we mean that literally. AI is not a feature we bolt on. It's not a marketing layer. It's not a roadmap experiment. It is the foundation of how we design, build, and operate. We are building systems where machines do what machines do best: pattern recognition, synthesis, analysis at scale. As well as what humans do what humans do best: judgment, context, trust, and accountability. That means rethinking workflows from the ground up. Not "how do we add AI to this process?" but "how should this process exist in a machine-augmented world?" We care deeply about shipping real systems that work in production. In regulated environments. With real customers. At scale. If you're excited to help invent the next way software is built and deployed, and to do it alongside a team of deeply pragmatic, AI-obsessed builders, we'd love to talk. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Job Description Job Description Company Description About AbbVie AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at . on LinkedIn, Facebook, Instagram, X and YouTube. Job Description As a discipline expert and technology leader, the Principal AI Engineer II will define and advance the strategy, architecture, and engineering practices required to develop, deploy, and scale artificial intelligence solutions across AbbVie. This role will investigate, identify, and implement state-of-the-art technology platforms that drive productivity and efficiency gains in own function and throughout multiple business areas. Technical leader acting at a group, department, and cross-functional levels. Responsible for managing the Data, Solutions, Business, and/or technology environments and tying them to the Enterprise Architecture and other architectures designs. Responsibilities: Define and advance AI engineering strategy, architecture, standards, and roadmaps aligned with AbbVie's business and technology objectives. Architect and build secure, scalable, reusable AI platforms, services, developer tools, and components that support enterprise adoption. Lead production-grade AI solutions from ideation and prototyping through development, testing, validation, deployment, monitoring, continuous improvement, and retirement. Integrate AI platforms with AWS services and enterprise data, software, security, identity, and infrastructure environments. Establish practices for evaluation, testing, versioning, release management, observability, performance monitoring, incident response, and operational support. Apply risk-based approaches to compliance, GxP, data integrity, privacy, cybersecurity, documentation, traceability, human oversight, and responsible AI. Partner with Quality, Regulatory, Legal, Privacy, Information Security, scientific, technical, and business stakeholders to deliver fit-for-purpose solutions. Serve as a trusted technical advisor, facilitate alignment, influence decisions without direct authority, and communicate complex tradeoffs and recommendations to technical and non-technical audiences. Evaluate emerging technologies, lead innovation and proof-of-concept efforts, and guide promising solutions into sustainable production capabilities. Mentor engineers, advance engineering maturity, promote knowledge sharing, and represent AbbVie in relevant technical communities and partner engagements. Qualifications Required Bachelor's degree with 9 years' experience, Master's degree with 8 years' experience, or PhD with 4 years' in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related technical discipline. Demonstrated experience designing, deploying, and operating production-scale AI and machine learning systems. Strong experience in AI/platform engineering, including scalable platforms, reusable components, and production-grade AI solutions. Demonstrated experience designing, building, deploying, and supporting solutions using AWS systems and services. Strong software and cloud engineering practices, including secure design, automated testing, CI/CD, containerization, monitoring, and operational support. Experience working in a regulated environment, preferably pharmaceutical, biotechnology, healthcare, medical device, or life sciences. Experience supporting the full solution lifecycle, including feasibility, design, development, testing, validation, deployment, and ongoing operations. Strong understanding of risk management, validation, change control, data integrity, cybersecurity, privacy, documentation, and quality requirements. Demonstrated ability to manage complex stakeholders, influence technical and business decisions, and collaborate across organizational boundaries. Excellent communication skills with senior leaders, technical teams, scientific experts, business partners, and external collaborators. Demonstrated innovation and mentoring experience, including advancing engineering practices and delivering measurable impact. Preferred Experience with enterprise AI governance, responsible AI, model validation, human oversight, and AI lifecycle management. Experience with MLOps and production engineering, including model evaluation, APIs, microservices, CI/CD, observability, and infrastructure as code. Open-source contributions or technical thought leadership through publications, patents, or industry working groups. Experience leading cross-functional product development from feasibility through validation and production implementation. Enterprise AI or platform experience, including AWS architecture, governance, security, and integration with enterprise environments. Additional Information Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future. We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees. This job is eligible to participate in our long-term incentive programs. Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company's sole and absolute discretion, consistent with applicable law. AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled. US & Puerto Rico only - to learn more, visit -us/equal-employment-opportunity-employer.html US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more: -us/reasonable- accommodations.html