AI/ML Engineer

This is a fully remote position, open to applicants in Colorado.

📋 Description

• Create, develop, and implement AI/ML models and pipelines that fulfill mission and performance goals.

• Construct, train, and refine models utilizing frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain.

• Design and operationalize MLOps pipelines using MLflow, Kubeflow, DVC, or custom training/inference orchestration methods.

• Implement and enhance vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid).

• Produce clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services.

• Experiment with the fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT).

• Contribute to agent-based applications through frameworks like LangGraph, AutoGen, CrewAI, or DSPy.

• Integrate AI services into practical systems via APIs, event-driven workflows, or UI copilots.

• Work collaboratively with data engineers, software developers, and mission analysts to ensure AI models are production-ready and meet customer requirements.

• Engage in peer reviews, contribute to communal repositories, and document models and experiments to ensure reproducibility.

⛳️ Requirements

• Must be a U.S. citizen and willing to obtain and maintain a security clearance, if necessary.

• 6-10+ years of professional experience in developing and deploying AI/ML solutions in production settings.

• A minimum of 3 years' professional experience within the Department of Defense/Department of War (DoD/DoW) AI assurance, security, and deployment environments.

• Strong Python development skills with direct experience in building AI/ML solutions.

• Hands-on experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, or LangChain.

• Proven capability in building and deploying MLOps pipelines using MLflow, Kubeflow, DVC, or similar tools.

• Familiarity with vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval-based architectures (RAG, hybrid, graph).

• Professional experience in fine-tuning and evaluating LLMs or smaller task-specific models using LoRA, QLoRA, or PEFT.

• Experience integrating AI capabilities into production systems or mission applications.

• Knowledge of 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.

• An active Secret clearance is preferred; the ability to obtain one is required.

🏝️ Benefits

• Telecommute

• Health insurance

• Professional development opportunities