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# AI Engineer (Python/LLM/Data Parsing) - 50% telework

- **Company:** Linchpin Software LLC
- **Location:** Chantilly, VA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $130,000 - $230,000
- **Contract:** Permanent contract
- **Skills:** LanceDB, Artificial Intelligence, Computer Vision, Continuous Integration, Data Structures, Distributed Systems, Graph Database, Python (Programming Language), PostgreSQL, Neo4j, Named Entity Recognition, Parsing, Tensorflow, Secure Coding, Unstructured Data, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Agentic-AI, Fastapi, Pgvector, Scikit Learn, Kubernetes, Low Latency, HuggingFace, Ollama, Machine Learning Operations, Entity Resolution, VLLM, Microservices
- **Published:** November 9, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9299692/ai-engineer-pythonllmdata-parsing-50-telework

## About the Role

Active Top Secret, TS/SCI, or Intel Agency clearance

 - Bachelor's degree
 - 10+ years of engineering experience with substantial hands-on AI/ML work
 - Advanced Python, with strong algorithms, data structures, and distributed systems fundamentals
 - Hands-on experience deploying production GenAI/LLM systems, embeddings, and vector databases
 - PyTorch, TensorFlow, or scikit-learn
 - MLOps tooling (MLflow or similar), containers, and Kubernetes
 - Background in NLP, computer vision, or a related AI field
 - Clear communication and a genuine willingness to help customers and teammates

Preferred:

 - Self-hosting open models on GPUs (vLLM, Ollama, Hugging Face, or similar)
 - Document parsing and extraction at scale
 - Vector and graph databases (LanceDB, pgvector, Neo4j, or similar)
 - FastAPI, PostgreSQL, Temporal.io
 - Geospatial, temporal, or computer vision AI
 - Air-gapped, multi-enclave, or cross-domain environments
 - AI governance and secure development practices

## Description

Build production AI/ML and GenAI pipelines, models, and microservices in Python

 - Build LLM applications: embeddings, vector databases, retrieval, agentic workflows, and tool calling
 - Engineer complex data parsing and ingestion for diverse structured and unstructured data
 - Implement named entity recognition, entity extraction, and entity resolution
 - Deliver multi-step retrieval and structured output with deep source citation
 - Train, evaluate, and serve models (PyTorch, TensorFlow, scikit-learn) with MLOps and CI/CD
 - Deploy on Kubernetes and locally hosted GPU servers, and tune for accuracy, latency, and cost