AI Engineer

Responsibilities

• Integrate conversational AI assistant features into software applications, owning the experience end to end
• Design, implement, and evaluate tool/function calling so AI assistants can reliably invoke backend capabilities and return correct, useful results
• Write, test, and refine prompts; evaluate and improve the accuracy and effectiveness of LLM and tool-calling behavior
• Build, train, and evaluate machine learning models for classification, prediction, and simulation tasks
• Collaborate with software engineers to integrate AI capabilities into applications and services
• Use Git for version control and contribute to shared team repositories
• Document your work and communicate results clearly to technical and non-technical stakeholders
• Support deployment, testing, and iteration of AI capabilities alongside the broader engineering team

• Required Bachelor's degree in Computer Science, Data Science, Machine Learning, Engineering, Mathematics, or a related field, or equivalent practical experience
• Experience integrating large language models via APIs, including function/tool calling and structured (e.g., JSON) outputs
• Foundation in machine learning and data science concepts (supervised learning, model evaluation, statistics)
• Working knowledge of Git and collaborative software development workflows (version control, branching, code review)
• Strong problem-solving skills and the ability to work effectively on a cross-functional engineering team
• U.S. person (per ITAR/EAR) able to obtain and maintain a U.S. Government Secret clearance
• Preferred Strong proficiency in Python and/or C# (depth in either is welcome; both is a plus)
• Experience building chat/assistant interfaces or agentic systems, ideally in production
• Familiarity with tool-calling/agent frameworks, retrieval-augmented generation (RAG), and vector databases
• Experience defining evaluations or test harnesses to measure and improve LLM and tool-calling reliability
• Internship, academic, or project experience building and deploying models or AI features end-to-end
• Exposure to software engineering fundamentals: APIs, testing, CI/CD, containerization
• Use of Git Lab for issue tracking and software configuration management
• Familiarity with secure AI/compute environments