AI/ML Engineer

Role overview

Responsibilities

• check_circle Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives.
• check_circle Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain.
• check_circle Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration).
• check_circle Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid).
• check_circle Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services.
• check_circle Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT).
• check_circle Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy.
• check_circle Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots.
• check_circle Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs.
• check_circle Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility.
• check_circle Must be a U.S. citizen and be willing to obtain and maintain a secruity clearance, as needed.
• check_circle 6-10+ years of professional experience developing and deploying AI/ML solutions in production environments.
• check_circle Professional experience within the Department of Defense (DoD/DoW) AI assurance, security, and deployment environments.
• check_circle Strong Python development skills with hands-on experience building AI/ML solutions.
• check_circle Direct experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, or LangChain.
• check_circle Proven ability to build and deploy MLOps pipelines using MLflow, Kubeflow, DVC, or equivalent.
• check_circle Working knowledge of vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval-based architectures (RAG, hybrid, graph).
• check_circle Professional experience fine-tuning and evaluating LLMs or smaller task-specific models using LoRA, QLoRA, or PEFT.
• check_circle Professional experience integrating AI capabilities into production systems or mission applications.

Preferred 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.
• Active Secret clearance preferred; ability to obtain one is required.