AI/ML Engineer

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

📋 Description

• Design, create, 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.

• Develop and operationalize MLOps pipelines using tools like MLflow, Kubeflow, DVC, or custom training/inference orchestration solutions.

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

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

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

• Contribute to agent-based applications using frameworks such as LangGraph, AutoGen, CrewAI, or DSPy.

• Integrate AI services into real-world systems through APIs, event-driven workflows, or UI copilots.

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

• Engage in peer reviews, contribute to shared 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 as required.

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

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

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

• Direct experience with ML frameworks like PyTorch, TensorFlow, scikit-learn, Hugging Face, or LangChain.

• Proven capability to build and deploy MLOps pipelines using MLflow, Kubeflow, DVC, or similar tools.

• Working knowledge of 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.

• Professional experience in integrating AI capabilities into production systems or mission applications.

🏝️ Benefits

• Flexible work arrangements

• Professional development

• Work from home options