design, create, and implement complete AI systems that will transform client operations
increase data accessibility
optimize AI and ML systems
ensure that your team’s solutions consider the broader ecosystem and operating environment as well as future functionality and enhancements
deepen your skill set in areas like software engineering, machine learning operations (MLOps), and software deployment and integration into a variety of different mission environments

3+ years of experience as a ML engineer and building production-grade ML solutions, including work involving LLMs, agents, or complex automation frameworks
3+ years of experience working within data science or data research in a professional or academic environment, and training or deploying models across multiple modalities of data
3+ years of experience working in cloud environments, including AWS and Azure
2+ years of experience deploying and integrating production-grade ML models using tools, such as Docker and Kubernetes
Experience with Large Language Models (LLM), Deep Learning (DL), and Reinforcement Learning (RL), and with tools and AI agent frameworks such as LangChain, LangGraph, PydanticAI, or llamaindex
Experience in connecting Agents to APIs, Cloud platforms, or databases
Experience evaluating LLM performance and building observation layers for stakeholders, including Grafana, Langfuse, LangSmith, or Phoenix
Experience evaluating architectural tradeoffs and designing robust service-based software applications for scalable use
Ability to obtain a Secret clearance
Bachelor’s degree

AI Machine Learning Engineer

Key skills