Note: The job is a remote job and is open to candidates in USA. Minfy is hiring a Senior AI / LLM Engineer to design and build LLM-powered features and applications across enterprise use cases. The role owns systems end-to-end, including RAG pipelines, data ingestion, LLM and platform integrations, evaluation, production hardening, observability, and mentoring other engineers.
Responsibilities
- 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
Skills
- Must be a U.S. Person (required for access to an ITAR / export-controlled environment)
- • 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
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