AI Engineer

Location: McLean, VA

Eligibility: Must be a U.S. Person (required for access to an ITAR / export-controlled

environment)

About the Role

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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.