Note: The job is a remote job and is open to candidates in USA. Convergenz is seeking a Generative AI Engineer to build LLM and document-intelligence capabilities on a greenfield data and AI platform within a high-trust federal environment. The role involves hands-on work to create product demonstrations and migrate documented processes to the internal team.
Responsibilities
- Build RAG and document-processing pipelines on the Databricks lakehouse - ingestion, OCR of mixed-quality sources, chunking, embedding, and retrieval
- Build LLM workflows for summarization, structured extraction, and evidence-grounded generation with source attribution
- Generate synthetic document corpora with the fidelity and quality variation needed for meaningful results
- Stand up the evaluation harness - retrieval quality, groundedness and hallucination checks, structured-output validity, human-in-the-loop review. Report results in numbers
- Package deliverables as jobs and Asset Bundles, tracked in MLflow, and document everything the internal team needs to own the work
Skills
- U.S. citizenship
- [8]+ years building applied ML/AI or data systems, with demonstrated delivery of LLM and RAG systems you personally built - not notebook demos
- Hands-on Databricks
- Document processing at scale: OCR, layout-aware parsing, chunking tradeoffs, poor-quality source handling
- Local/self-hosted LLM serving - vLLM, TGI, Ollama, llama.cpp, or equivalent - including running open-weight models in an isolated or air-gapped environment without reliance on external API endpoints
- Structured extraction and grounded generation with source attribution
- LLM evaluation methodology - you can explain how you measured correctness and what the evaluation missed
- Privacy-preserving synthetic data generation from CUI, PII, or comparably restricted source data, with an understanding of re-identification risk
- Strong Python
- Government or defense contracting experience
- RAG built inside a government or FedRAMP-authorized environment (Azure OpenAI in GCC High, AWS GovCloud, Bedrock within an authorized boundary)
- Direct experience with FedRAMP Moderate, NIST 800-171, CMMC L2, or CUI handling
- Databricks Vector Search, Mosaic AI Agent Framework and Agent Evaluation, Asset Bundles, MLflow
- Unity Catalog governance
- H2O (h2oGPTe, Driverless AI)
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