Applied AI​/ML Engineer; Intelligence Systems

Description

Leidos is seeking an Applied AI/ML Engineer to support a large, mission-critical U.S. Navy program. This role focuses on designing and building AI‑and machine learning‑enabled performance intelligence systems that help identify operational risks, diagnose systemic performance issues, and surface improvement opportunities across complex program operations.

The ideal candidate is a systems-oriented engineer with strong Python development skills and applied data science experience who enjoys working on messy real-world operational problems. Rather than performing one-off analyses, this role focuses on building durable analytical capabilities and software systems that continuously analyze performance data and enable more proactive program management.

This position sits within the program’s Performance Management team, supporting Service Level Requirements (SLRs) and broader Navy performance initiatives such as World Class Alignment Metrics (WAM). The work combines systems thinking, applied analytics, and production-quality software engineering to improve how performance issues are detected, understood, and addressed across the program. This team operates in a code-first environment where analytical capabilities are developed as maintainable software systems rather than one-off analyses.

• 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.

• 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…