AI/ML Engineer
RemoteUnited States
Job Summary
Design, develop, and deploy AI/ML models and pipelines meeting mission objectives using PyTorch, TensorFlow, and LangChain. Build MLOps workflows with MLflow, Kubeflow, and DVC while optimizing vector databases and retrieval architectures for RAG and graph systems. Write efficient Python code for data ingestion, feature engineering, and inference services, then integrate AI capabilities into production systems via APIs or event-driven workflows. Experiment with fine-tuning LLMs using LoRA, QLoRA, and PEFT, and contribute to agent-based applications with LangGraph or AutoGen. Collaborate with data engineers and mission analysts to ensure models are production-ready, participate in peer reviews, and document experiments for reproducibility. Requires U.S. citizenship, security clearance, and 6-10+ years of experience in DoD/DoW environments.
Required Qualifications
- U.S. citizen
- willing to obtain and maintain a security clearance
- 6-10+ years of professional experience developing and deploying AI/ML solutions in production environments
- 3 years' professional experience within the Department of Defense/Department of War (DoD/DoW) AI assurance, security, and deployment environments
- Strong Python development skills
- Direct experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, or LangChain
- Proven ability to build and deploy MLOps pipelines using MLflow, Kubeflow, DVC, or equivalent
- Working knowledge of vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval-based architectures (RAG, hybrid, graph)
- Professional experience fine-tuning and evaluating LLMs or smaller task-specific models using LoRA, QLoRA, or PEFT
- Professional experience integrating AI capabilities into production systems or mission applications
- Active Secret clearance
- ability to obtain one
Desired Qualifications
- Familiarity with agentic frameworks (LangGraph, AutoGen, CrewAI, DSPy) and multi-agent reasoning
- Understanding of prompt engineering, retrieval quality, and grounding methods
- Exposure to GPU-based or edge inference environments
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field
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