AI Architect & RAG Data Science Engineer

Working with technical and program leadership, the incumbent contributes hands-on expertise across AI/ML, software, cloud, cyber, data engineering, systems engineering, and test. Mission and architecture delivery: Translate operational, test, and sustainment needs into RAG solution designs, delivery increments, acceptance criteria, and measurable decision-support outcomes under established program priorities. Secure RAG and agent implementation: Design and implement capabilities spanning source onboarding, document parsing and normalization, metadata and taxonomy management, hybrid retrieval, reranking, LLM inference, citations, guardrails, and agentic workflows using controlled tool use and handoffs; apply llama.cpp or comparable local inference runtimes where permitted. Search and retrieval engineering: Apply search methods including BM25, TF-IDF, embeddings, hybrid retrieval, metadata filtering, and reranking to improve recall, precision, traceability, and user trust in technical knowledge retrieval. Data science and assurance: Develop data-quality, retrieval-relevance, groundedness, faithfulness, latency, usefulness, and safety evaluations; maintain curated test sets, analytic baselines, experiments, and performance dashboards to support evidence-based releases. Full-stack delivery: Contribute to secure user experiences, RESTful APIs, application services, workflow orchestration, data stores, vector databases, integration patterns, and observability needed to operate an AI product at enterprise scale. Data and telemetry integration: Partner with engineering, test, and data owners to integrate structured and unstructured technical data, test artifacts, logs, sensor or platform telemetry, and operational knowledge while preserving provenance and access controls. DevSecOps and MLOps: Apply Git-based development, automated testing, CI/CD, containerization, vulnerability management, model and data versioning, monitoring, auditability, and repeatable deployment across approved environments. Collaboration and complex problem resolution: Help decompose ambiguous technical problems involving fragmented data, conflicting sources, constrained networks, evolving requirements, performance tradeoffs, and mission risk; document findings and communicate workable alternatives to technical and program stakeholders. Performs contractor technical support and advisory functions only; command, acquisition, operational, and inherently governmental decisions remain with authorized government officials. Work may require onsite presence at government or company facilities, collaboration across secure and non-secure networks, CONUS or OCONUS travel, surge support, and adjustment to evolving mission priorities. Four or more years of professional experience delivering software, data, analytics, AI/ML, cloud, or data-platform capabilities in an enterprise, regulated, mission-critical, or defense-adjacent environment. Bachelor's degree in artificial intelligence, machine learning, data science, computer science, engineering, applied mathematics, or a related discipline; equivalent relevant experience may be considered where permitted by contract. Deep practical understanding of agentic AI and RAG systems, including tool use, multi-step orchestration, retrieval design, prompt and model orchestration, source traceability, evaluation, and responsible-AI controls; experience with llama.cpp and OpenCode or comparable approved tools. Working knowledge of search and retrieval algorithms including BM25, TF-IDF, vector embeddings, hybrid retrieval, metadata filtering, and reranking. Strong programming and engineering foundation using Python and at least one additional object-oriented language such as Java, C#, or comparable technologies; familiarity with Git, debugging, build workflows, and code review practices. Working knowledge of SQL, structured and unstructured data pipelines, APIs, service-oriented architectures, document processing, vector search, web or frontend integration, containers, CI/CD, and secure software development practices. Ability to communicate technical designs, delivery risk, test evidence, and operational implications clearly to technical, government, contractor, and nontechnical stakeholders. Master's degree or active graduate-level study in AI, ML, data science, computer science, engineering, or a closely related field. Experience with Kubernetes, cloud-native platforms, infrastructure as code, GitLab, GitHub Actions, Azure DevOps, or comparable automated delivery toolchains. Experience operating AI, analytics, telemetry, digital engineering, test, or sustainment solutions in Department of Defense, aerospace, aviation, or similarly constrained environments. Experience implementing model evaluation, observability, safety or policy guardrails, identity-aware access controls, and data or model provenance for generative-AI systems. Experience leading Agile or hybrid delivery teams and integrating AI products with enterprise systems, secure networks, edge deployments, or disconnected operations.