AI/ML Engineer
$137,000–$200,200 year
On-site · McLean, Virginia, United States
Job Summary
AI/ML Engineer responsible for developing, implementing, and maintaining AI/ML-enabled applications with a focus on agent-based workflows, LLM integration, and retrieval-augmented generation. Design and support agent-based architectures, orchestration logic, tool use, memory management, and integration with external systems. Build and maintain RAG pipelines (document processing, embedding generation, retrieval configuration, prompt assembly, response optimization) and integrate LLMs via APIs and AI frameworks. Develop REST APIs (Python-based services) to support data retrieval and enterprise integration, and contribute to full-stack production-ready applications including backend services and user-facing components. Design/refine Postgres schemas for scalable data models, and optimize applications for performance, reliability, and maintainability in enterprise environments. Minimum qualifications include a bachelor’s degree, TS/SCI with polygraph, on-site work readiness, US citizenship, and strong Python/data/AI skills; preferred qualifications cover MCP tooling, full-stack agent-based applications, FastAPI/Flask, memory and context management, observability, vector databases, Docker/AWS, and Postgres data processing.
Required Qualifications
- Bachelor’s degree in computer science or related area of study.
- Active/current TS/SCI with required polygraph.
- Willingness to work onsite full time.
- US citizenship required.
- Proficiency in Python and common data/AI libraries.
- Experience developing AI/ML applications, including LLM-integrated applications, RAG workflows, or similar retrieval-based capabilities.
- Experience with agent-based programming concepts, AI frameworks, and tool-based LLM application development.
- Strong understanding of AI/ML foundational concepts, including transformers, algorithms, LLM behavior, data processing, and model integration patterns.
- Experience designing, developing, or integrating REST APIs to retrieve, process, or exchange data across systems.
- Familiarity with production-ready application development practices, including reliability, maintainability, testing, and deployment considerations.
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