> Markdown version of [/jobs/ext/3548675-ai-engineer-job-in-mclean](https://www.wearedevelopers.com/jobs/ext/3548675-ai-engineer-job-in-mclean). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer job in McLean - **Company:** Minfy Technologies, Inc. - **Location:** McLean, VA, United States - **Experience:** Expert - **Salary:** $150,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Collaborative Software, Continuous Integration, Extract Transform Load (ETL), Data Security, Software Debugging, Entity Relationship Models, Graph Database, Python (Programming Language), OAuth, OpenAI, Software Deployment, Software Engineering, Data Logging, Cloud Platform System, Data Ingestion, Retrieval-Augmented Generation, Large Language Models, Generative AI, Indexer, Backend, Build Management, Question Answering, Kubernetes, Information Technology, Amazon Bedrock, Wikis, Machine Learning Operations, Software Version Control, Data Pipelines, Docker - **Published:** October 1, 2026 - **Apply:** https://jobs.diversity.com/career/2510501/ai-engineer-virginia-va-mclean ## About the Role * 6-8 years of software engineering experience, with at least 2 years building with LLMs or applied ML in production. * Strong proficiency in Python (or comparable) and strong engineering fundamentals - testing, version control, clean and maintainable code. * Deep hands-on experience with RAG systems: embeddings, vector databases, chunking/indexing, and a strong track record diagnosing and improving retrieval quality. * Experience designing and building data ingestion/ETL pipelines over large, messy, real-world datasets. * Strong experience integrating third-party APIs into backend services, including authentication flows (OAuth/SSO) and webhook/event-driven patterns. * Hands-on experience with LLM APIs (e.g., Anthropic, OpenAI, or similar) and orchestration frameworks. * Experience building and relying on evaluations for model and retrieval outputs. * Experience with knowledge graphs or entity-relationship modeling for retrieval. * Experience building multi-user or multi-tenant systems with scoped permissions and audit requirements. * Familiarity with observability and tracing for LLM or data pipelines. * A rigorous approach to data access, permissions, and handling sensitive information. * Experience taking systems to production on a major cloud platform (AWS preferred), and a track record of owning features independently., * Experience with Amazon Bedrock or other managed LLM platforms. * Experience integrating with enterprise collaboration platforms (chat, wikis, ticketing) via their APIs. * MLOps exposure: Docker, CI/CD, production service deployment. * Bachelor's or advanced degree in Computer Science, Engineering, or a related field - or equivalent practical experience. ## Description We are hiring a Senior AI / LLM Engineer to design and build LLM-powered features and applications across a range of use cases. You will work hands-on across the modern AI engineering stack - retrieval, integration, evaluation, and production hardening - and take ownership of significant pieces of the system from design through deployment. Retrieval-augmented generation over large, real-world enterprise data is a prominent part of the work, alongside platform integration and LLM-driven analysis. You will set technical direction within your area, make sound trade-offs under ambiguity, and help raise the bar for engineers around you. What You'll Do * Own the design and delivery of LLM-powered features end-to-end - from problem framing and architecture through production deployment and iteration. * Build and tune retrieval-augmented generation (RAG) pipelines over large, heterogeneous enterprise data - ingestion, chunking, embeddings, indexing, and entity/relationship modeling - with a focus on retrieval accuracy and closing coverage gaps. * Design and build data ingestion and indexing pipelines that reliably capture content, map identities across systems, and support incremental/resumable sync at scale. * Integrate LLMs (via managed platforms such as Amazon Bedrock) for question answering, analysis, and other tasks, preserving sessions, sources, and citations. * Integrate with enterprise platforms and collaboration tools through their APIs, including SSO/OAuth flows and event-driven bot/app patterns. * Design permission-bounded access and correct attribution in multi-user contexts, so the system never surfaces data a user could not already access. * Establish evaluation practices for retrieval quality and answer correctness, and use them to drive iteration and catch regressions. * Add observability, logging, and audit trails, and lead debugging of quality and performance issues in production. * Guide and mentor other engineers through design and code reviews, and contribute to shared standards.