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.

Responsibilities

• 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 with LLMOps to 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!

Qualifications

• 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, or a 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, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex, 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 for AI-based systems.
• Experience with structured and unstructured data, document processing, and application integration with existing enterprise systems.
• Understanding of responsible AI principles including safety, fairness, privacy, and model risk mitigation.
• Strong analytical and communication skills with the ability to collaborate across engineering, design, and mission domains.
• A collaborative mindset and a commitment to raising the technical quality of development work.
• Ability to hold a position of public trust with the U.S. government.