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# AI Engineer

- **Company:** MarineTraffic
- **Location:** United States
- **Salary:** $26,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Data Systems, Information Extraction, Python (Programming Language), OpenAI, Search Technologies, Retrieval-Augmented Generation, Large Language Models, Ollama, Machine Learning Operations, Virtual Agents, Evaluation of Large Language Models, Databricks, Web Api
- **Published:** October 2, 2026
- **Apply:** https://www2.jobdiva.com/portal/?a=j7jdnwgck4wulo816y7l865pzq556q05f5cqdk65b0o0dpy0m5p196w9a9ln1g3w&compid=0/jobs/33188335#/jobs/33188335

## About the Role

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.
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).

Soft Skills
Self-directed execution against a fixed milestone with minimal oversight.
Honest reporting of model behavior - comfortable saying what an evaluation does and does not establish.
Collaboration across technical and non-technical teams.
Clear documentation and active knowledge transfer.

## 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.


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