Senior Software Development Engineer II - Agentic AI & LLM p

Strategic Analysis, Inc. is an Equal Opportunity Employer.





Senior Software Development Engineer II - Agentic AI & LLM platforms (#26-074)

Job Code:26-074
Location:Remote
FT/PT Status:Full Time
Required Clearance:Public Trust

  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.

Strategic Analysis, Inc. is an Equal Opportunity employer and is committed to non-discrimination in employment. All qualified applicants will receive consideration for employment without regard to race, color, religions, sex (including pregnancy, sexual orientation, or gender identity), national origin, disability (physical or mental), age (40 or older), protected veteran status, genetic information (including family medical history) or any other characteristic protected by law. This policy includes but is not limited to the following employment actions: recruitment, hiring, firing, promotion, demotion, compensation, fringe benefits, training, mentoring and sponsorship programs.