18 jobs found

Job Description Top Skills: Enterprise AI Architecture (Model gateways, Model routing, Shared AI services, Platform engineering, AI service abstraction layers, Multi-model strategies, Vendor portability, AI observability, Platform scalability, Tenant isolation, AI FinOps and cost controls) RAG and Knowledge Architecture Generative AI & Agentic AI Architecture Must have previous experience as an Architect implementing A.I into an Enterprise Environment Must have experience with Enterprise AI Platforms Enterprise Standards and Patterns Enterprise AI Strategy Target-State Architecture Architecture Governance Executive Influence Description The AI Enterprise Architect is responsible for defining, evolving, and driving the enterprise architecture for artificial intelligence across the environment. This is not a theoretical, advisory-only, or documentation-focused architecture role. The AI Enterprise Architect must be a hands-on technology leader who can move rapidly from an ambiguous business problem to an executable architecture, working reference implementation, production deployment, and measurable business outcome. The role operates at the intersection of enterprise architecture, AI engineering, data, security, cloud platforms, integration, product delivery, and business strategy. This individual will establish the enterprise direction for generative AI, agentic AI, machine learning, intelligent automation, AI-enabled applications, and shared AI platform capabilities. The AI Enterprise Architect will work within a Fortune 500 environment while supporting an AI program that operates with the urgency, experimentation, adaptability, and delivery expectations of a startup. The successful candidate must be comfortable making architecture decisions in a rapidly evolving technology landscape, challenging conventional approaches, eliminating unnecessary complexity, and personally driving initiatives through organizational and technical barriers. This individual must be capable of seeing the entire enterprise AI ecosystem while remaining close enough to implementation to validate architectures, examine code and configurations, build prototypes, identify delivery risks, and distinguish production-ready capabilities from demonstrations and vendor claims. Key Responsibilities Enterprise AI Strategy and Target Architecture • Define and maintain the enterprise AI target architecture, transition architectures, capability model, platform strategy, and multiyear architecture roadmap. • Translate business strategy and operating priorities into executable AI capabilities, architecture investments, and delivery sequences. • Establish the architectural direction for generative AI, agentic AI, machine learning, intelligent automation, AI-assisted decision-making, and AI-enabled business processes. • Define clear boundaries and relationships between enterprise AI platforms, domain solutions, shared services, data platforms, enterprise applications, and external AI providers. • Ensure that project-level AI decisions support enterprise scalability, interoperability, security, reuse, and long-term maintainability. • Identify opportunities to consolidate overlapping technologies, eliminate duplicated capabilities, and prevent uncontrolled AI platform and vendor sprawl. • Develop architecture options and recommendations that explicitly address business value, delivery speed, cost, risk, technical debt, vendor dependency, and operational complexity. • Maintain a current enterprise view of AI capabilities, platforms, models, agents, integrations, data dependencies, vendors, risks, and strategic initiatives. Hands-On AI Architecture and Delivery • Lead AI initiatives from problem definition and architecture through implementation, production deployment, adoption, and measurable outcomes. • Develop working prototypes and reference implementations to validate architecture decisions, platform capabilities, integration approaches, security controls, and delivery feasibility. • Review source code, prompts, agent definitions, tool configurations, retrieval pipelines, model configurations, APIs, infrastructure, and deployment pipelines as needed to validate solution quality. • Work directly with engineering teams to resolve architecture and implementation issues rather than limiting involvement to reviews or recommendations. • Rapidly diagnose delivery blockers, simplify overengineered approaches, reduce unnecessary scope, and establish practical paths to production. • Define production-readiness criteria and ensure that AI solutions meet requirements for reliability, security, performance, observability, supportability, cost, and business continuity. • Distinguish clearly between proof of concept, pilot, minimum viable product, production capability, and enterprise platform. • Remain personally accountable for architecture outcomes, not only architecture artifacts or review completion. Generative and Agentic AI Architecture • Design enterprise-grade architectures for large language models, multimodal models, AI assistants, autonomous and semi-autonomous agents, and AI-enabled applications. • Define patterns for single-agent and multi-agent orchestration, tool use, planning, reasoning, memory, state management, delegation, and human approval. • Establish architecture standards for retrieval-augmented generation, structured retrieval, knowledge graphs, semantic search, and enterprise knowledge access. • Define patterns for context engineering, prompt management, structured outputs, model routing, fallback, caching, and workload segmentation. • Architect secure agent access to enterprise systems, APIs, data, workflows, and external services. • Define patterns for Model Context Protocol, agent-to-agent communication, enterprise APIs, event-driven interactions, and tool integration. • Establish controls around nondeterministic model behavior, including deterministic validation, approval checkpoints, execution boundaries, and exception handling. • Evaluate when AI agents are appropriate and when conventional software, workflow automation, rules engines, APIs, or analytics provide a better solution. • Prevent the use of generative AI or agents where the architecture introduces unnecessary cost, risk, latency, or operational complexity. Enterprise AI Platform Architecture • Define the architecture for shared enterprise AI platform capabilities, including model access, model gateways, agent runtime services, retrieval services, evaluation services, security controls, observability, and cost management. • Establish reusable AI services, platform components, reference architectures, templates, development patterns, and deployment patterns. • Define enterprise model access, model selection, model portability, workload routing, quota management, and vendor abstraction strategies. • Design workload, tenant, domain, environment, and data isolation patterns appropriate to enterprise risk and operating requirements. • Establish architectural standards for proprietary, open-weight, hosted, and internally operated models. • Define integration patterns between AI platforms and enterprise cloud, data, identity, security, integration, application, and observability platforms. • Partner with platform engineering, cloud infrastructure, data, cybersecurity, and application teams to establish a scalable AI operating environment. • Ensure that platform capabilities are implemented as usable products and services rather than architecture concepts that delivery teams cannot practically adopt. Data and Knowledge Architecture • Define data and knowledge architecture required to support AI models, agents, applications, evaluation, analytics, and business processes. • Establish patterns for structured, semi-structured, and unstructured data access. • Define architectures using relational, document, graph, vector, search, streaming, and analytical technologies based on workload requirements. • Establish standards for embeddings, chunking, indexing, metadata, reranking, retrieval, source attribution, and information freshness. • Define approaches for enterprise taxonomies, ontologies, semantic models, knowledge graphs, and reusable domain knowledge. • Ensure appropriate data lineage, provenance, ownership, quality, classification, access control, retention, and usage restrictions. • Define requirements for training, fine-tuning, inference, retrieval, evaluation, monitoring, and feedback datasets. • Ensure that AI responses and actions can be traced to authoritative enterprise information where required. • Identify situations where weak data, fragmented ownership, or poor knowledge management must be corrected rather than hidden behind an AI interface. Integration and Distributed Systems Architecture • Define and enforce AI integration patterns across enterprise applications, cloud platforms, SaaS products, data platforms, APIs, workflows, and external services. • Architect synchronous and asynchronous APIs, event-driven interactions, messaging, streaming, workflow orchestration, and long-running business processes. • Establish standards for identity propagation, delegated authorization, agent identity, workload identity, and service-to-service authentication. • Define system-of-record ownership . click apply for full job details

Job Description Top Skills: Enterprise AI Architecture (Model gateways, Model routing, Shared AI services, Platform engineering, AI service abstraction layers, Multi-model strategies, Vendor portability, AI observability, Platform scalability, Tenant isolation, AI FinOps and cost controls) RAG and Knowledge Architecture Generative AI & Agentic AI Architecture Must have previous experience as an Architect implementing A.I into an Enterprise Environment Must have experience with Enterprise AI Platforms Enterprise Standards and Patterns Enterprise AI Strategy Target-State Architecture Architecture Governance Executive Influence Description The AI Enterprise Architect is responsible for defining, evolving, and driving the enterprise architecture for artificial intelligence across the environment. This is not a theoretical, advisory-only, or documentation-focused architecture role. The AI Enterprise Architect must be a hands-on technology leader who can move rapidly from an ambiguous business problem to an executable architecture, working reference implementation, production deployment, and measurable business outcome. The role operates at the intersection of enterprise architecture, AI engineering, data, security, cloud platforms, integration, product delivery, and business strategy. This individual will establish the enterprise direction for generative AI, agentic AI, machine learning, intelligent automation, AI-enabled applications, and shared AI platform capabilities. The AI Enterprise Architect will work within a Fortune 500 environment while supporting an AI program that operates with the urgency, experimentation, adaptability, and delivery expectations of a startup. The successful candidate must be comfortable making architecture decisions in a rapidly evolving technology landscape, challenging conventional approaches, eliminating unnecessary complexity, and personally driving initiatives through organizational and technical barriers. This individual must be capable of seeing the entire enterprise AI ecosystem while remaining close enough to implementation to validate architectures, examine code and configurations, build prototypes, identify delivery risks, and distinguish production-ready capabilities from demonstrations and vendor claims. Key Responsibilities Enterprise AI Strategy and Target Architecture • Define and maintain the enterprise AI target architecture, transition architectures, capability model, platform strategy, and multiyear architecture roadmap. • Translate business strategy and operating priorities into executable AI capabilities, architecture investments, and delivery sequences. • Establish the architectural direction for generative AI, agentic AI, machine learning, intelligent automation, AI-assisted decision-making, and AI-enabled business processes. • Define clear boundaries and relationships between enterprise AI platforms, domain solutions, shared services, data platforms, enterprise applications, and external AI providers. • Ensure that project-level AI decisions support enterprise scalability, interoperability, security, reuse, and long-term maintainability. • Identify opportunities to consolidate overlapping technologies, eliminate duplicated capabilities, and prevent uncontrolled AI platform and vendor sprawl. • Develop architecture options and recommendations that explicitly address business value, delivery speed, cost, risk, technical debt, vendor dependency, and operational complexity. • Maintain a current enterprise view of AI capabilities, platforms, models, agents, integrations, data dependencies, vendors, risks, and strategic initiatives. Hands-On AI Architecture and Delivery • Lead AI initiatives from problem definition and architecture through implementation, production deployment, adoption, and measurable outcomes. • Develop working prototypes and reference implementations to validate architecture decisions, platform capabilities, integration approaches, security controls, and delivery feasibility. • Review source code, prompts, agent definitions, tool configurations, retrieval pipelines, model configurations, APIs, infrastructure, and deployment pipelines as needed to validate solution quality. • Work directly with engineering teams to resolve architecture and implementation issues rather than limiting involvement to reviews or recommendations. • Rapidly diagnose delivery blockers, simplify overengineered approaches, reduce unnecessary scope, and establish practical paths to production. • Define production-readiness criteria and ensure that AI solutions meet requirements for reliability, security, performance, observability, supportability, cost, and business continuity. • Distinguish clearly between proof of concept, pilot, minimum viable product, production capability, and enterprise platform. • Remain personally accountable for architecture outcomes, not only architecture artifacts or review completion. Generative and Agentic AI Architecture • Design enterprise-grade architectures for large language models, multimodal models, AI assistants, autonomous and semi-autonomous agents, and AI-enabled applications. • Define patterns for single-agent and multi-agent orchestration, tool use, planning, reasoning, memory, state management, delegation, and human approval. • Establish architecture standards for retrieval-augmented generation, structured retrieval, knowledge graphs, semantic search, and enterprise knowledge access. • Define patterns for context engineering, prompt management, structured outputs, model routing, fallback, caching, and workload segmentation. • Architect secure agent access to enterprise systems, APIs, data, workflows, and external services. • Define patterns for Model Context Protocol, agent-to-agent communication, enterprise APIs, event-driven interactions, and tool integration. • Establish controls around nondeterministic model behavior, including deterministic validation, approval checkpoints, execution boundaries, and exception handling. • Evaluate when AI agents are appropriate and when conventional software, workflow automation, rules engines, APIs, or analytics provide a better solution. • Prevent the use of generative AI or agents where the architecture introduces unnecessary cost, risk, latency, or operational complexity. Enterprise AI Platform Architecture • Define the architecture for shared enterprise AI platform capabilities, including model access, model gateways, agent runtime services, retrieval services, evaluation services, security controls, observability, and cost management. • Establish reusable AI services, platform components, reference architectures, templates, development patterns, and deployment patterns. • Define enterprise model access, model selection, model portability, workload routing, quota management, and vendor abstraction strategies. • Design workload, tenant, domain, environment, and data isolation patterns appropriate to enterprise risk and operating requirements. • Establish architectural standards for proprietary, open-weight, hosted, and internally operated models. • Define integration patterns between AI platforms and enterprise cloud, data, identity, security, integration, application, and observability platforms. • Partner with platform engineering, cloud infrastructure, data, cybersecurity, and application teams to establish a scalable AI operating environment. • Ensure that platform capabilities are implemented as usable products and services rather than architecture concepts that delivery teams cannot practically adopt. Data and Knowledge Architecture • Define data and knowledge architecture required to support AI models, agents, applications, evaluation, analytics, and business processes. • Establish patterns for structured, semi-structured, and unstructured data access. • Define architectures using relational, document, graph, vector, search, streaming, and analytical technologies based on workload requirements. • Establish standards for embeddings, chunking, indexing, metadata, reranking, retrieval, source attribution, and information freshness. • Define approaches for enterprise taxonomies, ontologies, semantic models, knowledge graphs, and reusable domain knowledge. • Ensure appropriate data lineage, provenance, ownership, quality, classification, access control, retention, and usage restrictions. • Define requirements for training, fine-tuning, inference, retrieval, evaluation, monitoring, and feedback datasets. • Ensure that AI responses and actions can be traced to authoritative enterprise information where required. • Identify situations where weak data, fragmented ownership, or poor knowledge management must be corrected rather than hidden behind an AI interface. Integration and Distributed Systems Architecture • Define and enforce AI integration patterns across enterprise applications, cloud platforms, SaaS products, data platforms, APIs, workflows, and external services. • Architect synchronous and asynchronous APIs, event-driven interactions, messaging, streaming, workflow orchestration, and long-running business processes. • Establish standards for identity propagation, delegated authorization, agent identity, workload identity, and service-to-service authentication. • Define system-of-record ownership . click apply for full job details

Job Description Description Looking for an opportunity to make an impact? At Leidos, we deliver innovative solutions through the efforts of our diverse and talented people who are dedicated to our customers' success. We empower our teams, contribute to our communities, and operate sustainable. Everything we do is built on a commitment to do the right thing for our customers, our people, and our community. Our Mission, Vision, and Values guide the way we do business. Are you ready for your next career challenge? Leidos Defense provides a diverse portfolio of systems, solutions, and services covering land, sea, air, space, and cyberspace for customers worldwide. Solutions for Defense include enterprise and mission IT, large-scale intelligence systems, command and control, geospatial and data analytics, cybersecurity, logistics, training, and intelligence analysis and operations support. Our team is solving the world's toughest security challenges for customers with "can't fail" missions. Leidos Defense is seeking a Mid-LevelSystems Engineer to join the IFPC Inc 2 systems engineering team. The IFPC Inc 2 is a ground-based, mobile system that aims to combat cruise missiles and unmanned aircraft systems. As part of the Indirect Fire Protection Capability program (IFPC), the system protects and defends civilian and military infrastructure and bridges the gap between tactical short range air defense systems and our strategic systems. This Systems Engineer will join a matrixed organization of systems engineers, model-based systems engineers, and specialty systems engineers. This systems engineer must have experience with developing systems engineering artifacts, including Requirements Specifications, Test Procedures, Test Reports, CONOPS, and Model-Based Systems Engineering (MBSE) content. The Systems Engineer will use Cameo Systems Modeler and SysML to develop requirements specifications, system architecture content (bdd / ibd), and system behavioral content (act / stm). This position does not necessarily require an MBSE Subject Matter Expert, but the role does minimally require experience working independently on MBSE models, some model customization, and the ability to communicate with non-Systems Engineering stakeholders who are less familiar with MBSE approaches. Specific roles and responsibilities include: Develop and manage the IFPC verification process & strategy Stand up a verification review board Own system level DVOs and VCNs Review and approve all DVOs and VCNs at the subsystem and below level including software Present metrics and status to SE lead and leadership weekly Plan verification priorities and manage a verification burndown against multiple configurations Ensure requirements are verifiable Manage the linkages between verification artifacts and requirements such as test procedures and analysis results Work to define success criteria Work a path towards addressing verification gaps Own the system level VCRM Mentor team in writing good verification artifacts Manage verification artifacts against multiple system configurations The Systems Engineer will work in a matrixed organization, which supports multiple programs at once in a variety of engineering fields with all different team environments. When the program ends, the Systems Engineer will have the opportunity to support a different program in the matrixed engineering portfolio. A candidate with experience in other systems engineering areas such as Model-Based Systems Engineering, Human Factors Engineering, Risk Management, and Reliability Analysis will be treated preferentially. However, a strong background in traditional systems engineering and technical leadership is what is required for this position. Basic Qualifications Bachelor's degree in Systems Engineering, Software Engineering, Computer Science, Computer Engineering, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or equivalent degree from an ABET-accredited university with 8+ years of experience or an MS with 6+ years. Experience in deriving requirements for complex electrical, electro-mechanical, mechanical, and software systems. Position requires candidates to be capable of performing in a collaborative engineering environment working closely with multi-disciplinary teams. Demonstrated experience effectively communicating within a team environment. Resident of the North Alabama area or willing to relocate to the Huntsville - North Alabama, AL area. Candidate must be a US Citizen and meet the eligibility to obtain (and maintain) a Secret Clearance Preferred Qualifications Preference shown to candidates who possess a Master of Science in System Engineering from an ABET-accredited university, and/or have at least 2-4 years of direct systems engineering experience. Preferred candidates will also have experience and/or working knowledge with: MagicDraw and/or other Model Based System Engineering (MBSE) modeling tools Demonstrated experience effectively leading a team of engineers. Demonstrated experience accurately estimating the engineering effort (i.e. hours) associated with Systems Engineering tasks. DOORS and/or other requirements database tools Understanding of the DoD acquisition processes and methods Experience supporting one or more DoD programs Object Management Group (OMG) Certified System Modeler Professional (OCSMP) certification INCOSE Systems Engineering Professional (SEP) certification (ASEP or CSEP) If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares. Original Posting:August 3, 2026 For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above. Pay Range:Pay Range $107,900.00 - $195,050.00 The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law. About Leidos Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. Headquartered in Reston, Virginia, with 47,000 global employees, Leidos reported annual revenues of approximately $16.7 billion for the fiscal year ended January 3, 2025. For more information, visit . Pay and Benefits Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available at -benefits. Securing Your Data Beware of fake employment opportunities using Leidos' name. Leidos will never ask you to provide payment-related information during any part of the employment application process (i.e., ask you for money), nor will Leidos ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Leidos will only communicate with you through emails that are generated by the automated system - never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Leidos that asks for payment-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at . If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission. Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.

Job Description Description Looking for an opportunity to make an impact? At Leidos, we deliver innovative solutions through the efforts of our diverse and talented people who are dedicated to our customers' success. We empower our teams, contribute to our communities, and operate sustainable. Everything we do is built on a commitment to do the right thing for our customers, our people, and our community. Our Mission, Vision, and Values guide the way we do business. Are you ready for your next career challenge? Leidos Defense provides a diverse portfolio of systems, solutions, and services covering land, sea, air, space, and cyberspace for customers worldwide. Solutions for Defense include enterprise and mission IT, large-scale intelligence systems, command and control, geospatial and data analytics, cybersecurity, logistics, training, and intelligence analysis and operations support. Our team is solving the world's toughest security challenges for customers with "can't fail" missions. We are seeking a highly motivated and experienced Product Owner with strong Agile expertise to join our team. The Product Owner will be responsible for defining and prioritizing product features, maintaining the team backlog, aligning with business and technical stakeholders, and ensuring delivery of high value, customer-focused solutions. This role requires strong communication, deep business understanding, and the ability to work effectively with Agile teams in a scaled environment. Primary Responsibilities Serve as the customer voice for the Agile team, ensuring alignment to business goals and program objectives. Define, maintain, and prioritize the team backlog (features, user stories, enablers). Develop and clearly communicate acceptance criteria for user stories and features. Collaborate closely with the Product Manager to refine features and ensure readiness for Program Increment (PI) planning. Contribute to PI Planning, Iteration Planning, Backlog Refinement, Iteration Reviews, and System Demos. Work with stakeholders and the System Architect to ensure backlog items meet Definition of Ready. Support the team in delivering high-quality increments that meet Definition of Done. Make real-time scope, priority, and trade-off decisions to maximize business value. Ensure transparency into progress, risks, and dependencies across the team and stakeholders. Analyze customer needs, market trends, and business objectives to support product roadmap decisions. Promote Agile SAFe practices to continuously improve team performance and outcomes. Basic Qualifications Bachelor's degree in Computer Science, Computer Engineering, or a related field with 8+ years of experience or 6+ with Master's 2+ years of experience as a Product Owner or similar role in an Agile environment. Experience writing user stories, features, defining acceptance criteria, and guiding cross-functional Agile teams. Experience decomposing large feature sets into smaller subsets for team execution. Strong communication, prioritization, and stakeholder engagement skills in a fast-paced, evolving environment. Currently living in Huntsville, AL or willing to relocate Currently holds or as the ability to obtain a Secret clearance Preferred Qualifications Experience working in a large-scale Agile SAFe environment. SAFe Product Owner/Product Manager (POPM) certification. If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares. Original Posting:July 17, 2026 For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above. Pay Range:Pay Range $107,900.00 - $195,050.00 The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law. About Leidos Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. Headquartered in Reston, Virginia, with 47,000 global employees, Leidos reported annual revenues of approximately $16.7 billion for the fiscal year ended January 3, 2025. For more information, visit . Pay and Benefits Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available at -benefits. Securing Your Data Beware of fake employment opportunities using Leidos' name. Leidos will never ask you to provide payment-related information during any part of the employment application process (i.e., ask you for money), nor will Leidos ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Leidos will only communicate with you through emails that are generated by the automated system - never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Leidos that asks for payment-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at . If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission. Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.

Job Description Description Looking for an opportunity to make an impact? At Leidos, we deliver innovative solutions through the efforts of our diverse and talented people who are dedicated to our customers' success. We empower our teams, contribute to our communities, and operate sustainable. Everything we do is built on a commitment to do the right thing for our customers, our people, and our community. Our Mission, Vision, and Values guide the way we do business. Are you ready for your next career challenge? Leidos Defense provides a diverse portfolio of systems, solutions, and services covering land, sea, air, space, and cyberspace for customers worldwide. Solutions for Defense include enterprise and mission IT, large-scale intelligence systems, command and control, geospatial and data analytics, cybersecurity, logistics, training, and intelligence analysis and operations support. Our team is solving the world's toughest security challenges for customers with "can't fail" missions. We are seeking a highly motivated and experienced Product Owner with strong Agile expertise to join our team. The Product Owner will be responsible for defining and prioritizing product features, maintaining the team backlog, aligning with business and technical stakeholders, and ensuring delivery of high value, customer-focused solutions. This role requires strong communication, deep business understanding, and the ability to work effectively with Agile teams in a scaled environment. Primary Responsibilities Serve as the customer voice for the Agile team, ensuring alignment to business goals and program objectives. Define, maintain, and prioritize the team backlog (features, user stories, enablers). Develop and clearly communicate acceptance criteria for user stories and features. Collaborate closely with the Product Manager to refine features and ensure readiness for Program Increment (PI) planning. Contribute to PI Planning, Iteration Planning, Backlog Refinement, Iteration Reviews, and System Demos. Work with stakeholders and the System Architect to ensure backlog items meet Definition of Ready. Support the team in delivering high-quality increments that meet Definition of Done. Make real-time scope, priority, and trade-off decisions to maximize business value. Ensure transparency into progress, risks, and dependencies across the team and stakeholders. Analyze customer needs, market trends, and business objectives to support product roadmap decisions. Promote Agile SAFe practices to continuously improve team performance and outcomes. Basic Qualifications Bachelor's degree in Computer Science, Computer Engineering, or a related field with 8+ years of experience or 6+ with Master's 2+ years of experience as a Product Owner or similar role in an Agile environment. Experience writing user stories, features, defining acceptance criteria, and guiding cross-functional Agile teams. Experience decomposing large feature sets into smaller subsets for team execution. Strong communication, prioritization, and stakeholder engagement skills in a fast-paced, evolving environment. Currently living in Huntsville, AL or willing to relocate Currently holds or as the ability to obtain a Secret clearance Preferred Qualifications Experience working in a large-scale Agile SAFe environment. SAFe Product Owner/Product Manager (POPM) certification. If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares. Original Posting:July 17, 2026 For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above. Pay Range:Pay Range $107,900.00 - $195,050.00 The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law. About Leidos Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. Headquartered in Reston, Virginia, with 47,000 global employees, Leidos reported annual revenues of approximately $16.7 billion for the fiscal year ended January 3, 2025. For more information, visit . Pay and Benefits Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available at -benefits. Securing Your Data Beware of fake employment opportunities using Leidos' name. Leidos will never ask you to provide payment-related information during any part of the employment application process (i.e., ask you for money), nor will Leidos ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Leidos will only communicate with you through emails that are generated by the automated system - never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Leidos that asks for payment-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at . If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission. Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.