AI Engineer

Marathon TS, within its Risk Decision Group, is hiring an AI Engineer to help build LLM and document-intelligence capabilities on a greenfield data and AI platform for a high-trust federal environment. This is a hands-on, near-term product demonstration focused on delivering working pipelines and transferring knowledge to an internal team over a one-year contract (with an option to extend). The role requires active T5/SSBI clearance.

What you will do

• Build RAG and document-processing pipelines on the Databricks lakehouse, including ingestion, OCR for mixed-quality sources, chunking, embedding, and retrieval.
• Create LLM workflows for summarization, structured extraction, and evidence-grounded generation with source attribution.
• Develop synthetic document corpora with fidelity and quality variation that supports meaningful evaluation results.
• Stand up an evaluation harness covering retrieval quality, groundedness, hallucination checks, structured-output validity, and human-in-the-loop review, with results reported in numbers.
• Package deliverables as jobs and Asset Bundles, tracked in MLflow, and document everything needed for internal ownership.

Security and compliance

• U.S. citizenship and an active T5/SSBI federally adjudicated clearance are required.
• Top Secret clearance is required.

Qualifications

• 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 experience.
• Document processing at scale: OCR, layout-aware parsing, chunking tradeoffs, and handling poor-quality sources.
• Local or self-hosted LLM serving, such as vLLM, TGI, Ollama, llama.cpp, or equivalent, including running open-weight models in an isolated or air-gapped environment without relying on external API endpoints.
• Structured extraction and grounded generation with source attribution.
• LLM evaluation methodology, including how correctness was measured and what the evaluation missed.
• Privacy-preserving synthetic data generation from CUI, PII, or similarly restricted data, including understanding of re-identification risk.
• Strong Python.
• Government or defense contracting experience.

Preferred experience

• RAG built inside a government or FedRAMP-authorized environment (examples listed: Azure OpenAI in GCC High, AWS GovCloud, Bedrock within an authorized boundary).
• 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).

Role logistics

• Location: Remote
• Job type: Contract
• Engagement: 1-year engagement (option to extend)
• Pay: $82.00 - $92.00 per hour (USD)

Relevant technologies

• Databricks, Databricks lakehouse, RAG, OCR, embeddings, LLM
• vLLM, TGI, Ollama, llama.cpp
• MLflow, Asset Bundles