Machine Learning Engineer

Convergenz; Maryland; On-site; USD 150,000 - 230,000; Full time

Job summary

Convergenz in the United States (Maryland) seeks an experienced ML/AI engineer to build RAG and document-processing pipelines on the Databricks lakehouse, focusing on ingestion, OCR, chunking, embedding, and retrieval.

You will design LLM workflows for summarization, structured extraction, and evidence-grounded generation with source attribution, create synthetic data, and stand up a rigorous evaluation harness with numbers.

Qualifications

• 8+ years building applied ML/AI or data systems with delivery of LLM/RAG projects.
• Hands-on experience with Databricks.
• Document processing at scale: OCR and layout-aware parsing.

Responsibilities

• Build RAG and document-processing pipelines on the Databricks lakehouse.
• Develop LLM workflows for summarization, extraction, and source-attributed generation.
• Generate synthetic document corpora with high fidelity and controlled quality variation.
• Stand up the evaluation harness with retrieval quality, groundedness, and hallucination checks.

Skills

Databricks

Python

ML/AI systems

OCR

Tools

MLflow

Databricks Vector Search

VLLM

Job description

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.

Required Qualifications

• U.S. citizenship and active T5/SSBI federally adjudicated clearance required.
• [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.

Preferred Qualifications

• 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).

We are an Equal Opportunity Employer committed to a diverse and inclusive workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, gender identity, national origin, disability, or veteran status. We value diverse perspectives and actively seek to create an inclusive environment that celebrates the unique qualities of all employees.