Senior AI Developer

Overview

We are seeking a dynamic, forward-thinking Senior AI Software Engineer (GenAI, Agents & RAG) to assist in the design and implementation of enterprise-wide AI-powered tools, workflows, and practices across both internal operations and client delivery teams. This candidate will partner with our internal AI Lab, delivery teams, and back-office functions including Finance, HR, Recruiting, and BD to identify high-impact AI use cases, select or build fit-for-purpose tools, and train teams on their use to drive measurable gains in efficiency, quality, and innovation.

The ideal candidate brings hands‑on knowledge of today’s AI tools and platforms, including GenAI, MLOps, RAG, AutoML, LLMOps, and orchestration frameworks, and combines that technical acumen with a change agent’s mindset—capable of translating potential into real‑world outcomes across diverse functions like software development, CI/CD infrastructure, HCD workshop synthesis, data engineering, AI/ML development, and business operations.

• You’ll be helping transform a fast‑moving technology company with deep federal roots, a collaborative culture, and a commitment to innovation. Our AI Lab, AI and Data Exploitation team, and HCD experts are ready to work with you.
• You’ll help serve as a bridge, ensuring cutting‑edge AI capabilities are used not just by technologists, but by every part of the enterprise.
  
  Develop end‑to‑end AI solutions including LLM‑powered applications, predictive ML models, multi‑agent workflows, RAG pipelines, and specialized AI microservices.
• Implement reusable AI components, libraries, and APIs that streamline application development and accelerate delivery across programs.
• Integrate AI models with enterprise systems, APIs, data platforms, vector databases, and cloud‑native services to deliver scalable mission capabilities.
• Drive iterative experimentation, prototyping, and model improvement cycles in collaboration with Data Scientists and AI Evaluation Scientists.
• Design and implement advanced prompt strategies, context management layers, retrieval systems, and LLM orchestration logic.
• Build scalable inference services,optimize model performance, and collaborate withLLMOpsto enable robust deployment, monitoring, and continuous improvement.
• Translate user needs and mission workflows into intuitive, reliable AI‑powered features through active partnership with designers and product teams.
• Implement secure‑by‑design and trustworthy AI practices, including safety guardrails, input sanitization, content filtering, and integration of evaluation metrics.
• Contribute to internal AI frameworks, code patterns, and shared accelerators that raise delivery quality across the AI & Data Exploitation Practice.
• Participate in code reviews and support engineering excellence across multi‑disciplinary AI delivery teams.
• Stay current with emerging AI techniques, libraries, foundation models, and agent frameworks, evaluating their applicability to client missions.
• You will contribute to the growth of our AI & Data Exploitation Practice!

• Must be local to DC Metro Area, 2‑3 days/week in McLean Office.
• Bachelor’s, Master’s, or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, ora related field.
• 5+ years of hands‑on software engineering experience, with some exposure to AI/ML, generative AI, or LLM‑driven application development.
• Experience in Python and modern AI frameworks such as PyTorch, Tensor Flow, Hugging Face Transformers,Lang Chain,Llama Index, or similar.
• Demonstrated ability to design and develop production‑grade AI applications, including APIs, back‑end services, orchestration logic, and front‑end integrations (when needed).
• Experience implementing RAG architectures, embeddings, vector stores, and context retrieval patterns.
• Familiarity with multi‑agent orchestration frameworks, prompt engineering strategies, and advanced LLM interaction design.
• Strong understanding of cloud platforms (AWS, Azure, GCP), including compute, serverless services, and security fundamentals for AI workloads.
• Working knowledge of containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines…