Design, develop, and fine‑tune a variety of AI models
Design autonomous agents and multi‑step pipelines using LangChain, ReAct, tool‑calling, or custom orchestration; employ the Model Context protocol to manage stateful interactions
Build Retrieval‑Augmented Generation pipelines that combine external knowledge bases with LLMs to improve factual accuracy for warfighting applications
Implement end‑to‑end data pipelines, ETL processes, and back‑end services (Python, C/C++, Java) that feed data to models
Create CI/CD pipelines for model training, validation, containerized deployment (Docker/Kubernetes), and security scanning; maintain model registries, monitoring, and version control of context protocols
Produce rapid prototypes, run benchmarks, and conduct robustness/adversarial testing in realistic environments
Work closely with senior ML engineers, software developers, and government customers; mentor junior staff and contribute to design reviews and documentation
Stay current with emerging LLM architectures, agentic paradigms, PEFT/LoRA methods, and AI‑safety techniques; translate new research into operational capabilities

Bachelor’s degree in Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related field with at least three (3) years of relevant experience
MS degree in the same with at least one (1) year of relevant experience
You will be subject to a background investigation and must be able to obtain and maintain an active Department of War (DoW) security clearance
Proficiency in Python and at least one compiled language (C/C++ or Java)
Experience with REST/GraphQL APIs and containerization
Strong grasp of ML theory (supervised, unsupervised, reinforcement learning) and evaluation metrics
Hands‑on experience fine‑tuning LLMs and using frameworks such as Hugging Face Transformers, LangChain, or comparable agent tools
Familiarity with building RAG pipelines (vector stores, dense/sparse retrievers)
Experience applying PEFT/LoRA methods (e.g., LoRA, adapters) to large models
Understanding of Model Context protocols for managing model state across multi‑turn interactions
Experience building evaluation frameworks, benchmarks, or data quality pipelines
Experience with TensorFlow, PyTorch, or JAX; knowledge of data‑pipeline tools (Airflow, Prefect, Ray) is a plus
Awareness of DevSecOps practices (CI/CD, GitOps, container security scanning, model‑registry concepts) is desirable

Associate AI Engineer – Mission Innovation Lab

Key skills