Applied AI/ML Engineer (Performance Intelligence Systems)

Leidos is seeking an Applied AI/ML Engineer to support a large, mission-critical U.S. Navy program. The role focuses on designing and building AI- and machine learning-enabled performance intelligence systems that help identify operational risks and improve program management through continuous analysis of performance data.

Responsibilities:

• Design and build AI- and machine learning–enabled performance intelligence systems that continuously analyze operational performance data and identify emerging risks, degradation patterns, and improvement opportunities
• Design and implement analytical services, pipelines, and tooling in Python that incorporate AI/ML methods and transform operational data into continuously updated performance intelligence
• Build cloud-deployed analytical tools and services that enable automated or semi-automated detection of performance issues tied to contractual Service Level Requirements (SLRs)
• Translate messy operational challenges into practical analytical solutions, combining statistical methods, machine learning techniques, and domain-informed logic
• Engineer reusable analytical capabilities, frameworks, and software components that strengthen the team’s long-term ability to diagnose and improve operational performance
• Collaborate with performance analysts, engineers, and program stakeholders to frame problems and design data-driven approaches to improving program outcomes
• Investigate systemic performance issues and engineer tools that surface root causes, prioritization signals, and improvement opportunities
• Communicate technical insights and analytical findings clearly to both technical teams and program leadership
• Support broader Navy performance initiatives by extending analytical methods and tooling beyond individual SLR use cases when appropriate

Requirements:

• Bachelor's degree with 8+ years of experience applying data science, machine learning, or AI to real-world operational or performance problems (additional experience may be considered in lieu of degree)
• Strong Python development experience building maintainable, production-quality software
• Experience designing and implementing analytical pipelines, data processing workflows, or AI/ML-enabled analytical systems
• Experience working with large, messy, or heterogeneous operational datasets and extracting meaningful signals
• Experience deploying analytical code, pipelines, or services in cloud or production environments
• Experience developing containerized analytical applications and deploying services through CI/CD pipelines
• Experience building APIs or service interfaces that expose analytical capabilities or models
• Demonstrated ability to frame ambiguous operational problems and engineer practical analytical solutions
• Ability to clearly communicate analytical reasoning and technical insights to both technical and non-technical stakeholders
• Experience building and maintaining analytical systems or tools used operationally by other teams or stakeholders
• Active Secret clearance or higher
• Experience applying machine learning, statistical modeling, or anomaly detection techniques to operational or performance datasets
• Experience building analytical tools, services, or platforms used by operational teams or decision-makers
• Exposure to AI-enabled workflows, automation, or reusable analytics frameworks
• Familiarity with container orchestration platforms (Kubernetes, ECS, or similar) for deploying scalable analytical services
• Experience working in large operational programs or complex enterprise environments, particularly within government or defense programs
• Strong systems thinking and curiosity about how complex operational environments function and fail