AI Engineer

About the Role\nWe 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.\n\nWhat You’ll Do\n- Own the design and delivery of LLM-powered features end-to-end — from problem framing and architecture through production deployment and iteration.\n- 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.\n- Design and build data ingestion and indexing pipelines that reliably capture content, map identities across systems, and support incremental/resumable sync at scale.\n- Integrate LLMs (via managed platforms such as Amazon Bedrock) for question answering, analysis, and other tasks, preserving sessions, sources, and citations.\n- Integrate with enterprise platforms and collaboration tools through their APIs, including SSO/OAuth flows and event-driven bot/app patterns.\n- Design permission-bounded access and correct attribution in multi-user contexts, so the system never surfaces data a user could not already access.\n- Establish evaluation practices for retrieval quality and answer correctness, and use them to drive iteration and catch regressions.\n- Add observability, logging, and audit trails, and lead debugging of quality and performance issues in production.\n- Guide and mentor other engineers through design and code reviews, and contribute to shared standards.\n\nRequired Qualifications\n- 6–8 years of software engineering experience, with at least 2 years building with LLMs or applied ML in production.\n- Strong proficiency in Python (or comparable) and strong engineering fundamentals — testing, version control, clean and maintainable code.\n- Deep hands-on experience with RAG systems: embeddings, vector databases, chunking/indexing, and a strong track record diagnosing and improving retrieval quality.\n- Experience designing and building data ingestion/ETL pipelines over large, messy, real-world datasets.\n- Strong experience integrating third-party APIs into backend services, including authentication flows (OAuth/SSO) and webhook/event-driven patterns.\n- Hands-on experience with LLM APIs (e.g., Anthropic, OpenAI, or similar) and orchestration frameworks.\n- Experience building and relying on evaluations for model and retrieval outputs.\n- Experience with knowledge graphs or entity-relationship modeling for retrieval.\n- Experience building multi-user or multi-tenant systems with scoped permissions and audit requirements.\n- Familiarity with observability and tracing for LLM or data pipelines.\n- A rigorous approach to data access, permissions, and handling sensitive information.\n- Experience taking systems to production on a major cloud platform (AWS preferred), and a track record of owning features independently.\n\nPreferred Qualifications\n- Experience with Amazon Bedrock or other managed LLM platforms.\n- Experience integrating with enterprise collaboration platforms (chat, wikis, ticketing) via their APIs.\n- MLOps exposure: Docker, CI/CD, production service deployment.\n- Bachelor’s or advanced degree in Computer Science, Engineering, or a related field — or equivalent practical experience.