Strategic Analysis, Inc. (SA) Is seeking a Software Engineer to support the Agentic AI & LLM platform at Advanced Research Projects Agency for Health (ARPA-H) Overview: The GRACE team at ARPA-H is building the next generation of agentic AI to transform how the agency accelerates research, makes decisions, and ships products at scale. GRACE is ARPA-H's production AI assistant, and we are evolving it into an ecosystem of autonomous, multi-agent systems. The team is a small, startup-minded team that ships fast and owns what they build end-to-end. They are looking for an SDE II who is hungry to contribute to a real production system, not a sandbox. The best person for this role communicates clearly, collaborates without ego, and brings genuine empathy for the users whose work they are making better. The person is a self-starter with a high bar and a high sense of urgency. He or She plays well with others and makes the people around him or her better. Roles and Responsibilities: - Build Agentic AI Systems - Build LLM-Powered Features - Own the Backend - Contribute to Infrastructure Requirements: - 3+ years of professional software engineering experience building and operating production systems - Proven experience in high-velocity environments where you contributed to shipping real products end-to-end - Strong proficiency in Python and at least one other backend language; familiarity with modern backend frameworks and async patterns - Solid understanding of algorithms, data structures, distributed systems, and software design patterns - Experience building and operating systems on major cloud platforms (AWS, GCP, or Azure) - Experience with containerization (Docker) and working within CI/CD pipelines - Clear, direct communicator who gives and receives feedback well, works with empathy, and makes the people around them better - Self-starter with a high bar and high sense of urgency; you do not wait to be told what to do next Preferred Qualifications: - Hands-on experience building features on top of LLMs in production: tool-calling, RAG, multi-step reasoning, and context management - Familiarity with A2A (Agent-to-Agent) communication patterns and multi-agent orchestration frameworks - Familiarity with MCP at the client/consumer layer: how agents discover and invoke tools via MCP - Working knowledge of prompt engineering and LLM behavior across model families; you understand why Claude and GPT respond differently to the same prompt - Experience with LLM evaluation, grounding assessment, or regression testing for AI-powered systems - Awareness of token economics at the application layer: cost-per-query, context budget management, and prompt efficiency - Experience on Microsoft Azure: Azure Functions, API Management, Container Apps, or Azure OpenAI Service - Familiarity with secrets management, least-privilege access, and security-conscious engineering practices - Experience in startup or early-stage environments: comfort with ambiguity, rapid iteration, and wearing multiple hats - Experience in healthcare, life sciences, or other regulated domains is a plus but not required Education: Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience Location: Remote with occasional travel Clearance: Ability to obtain HHS Public Trust.
