Agentic AI and LLM Software Development Engineer, Senior

Agentic AI and LLM Applications Software Development Engineer, Senior

The Opportunity:

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 CE is ARPA-H’s production AI assistant, and we are evolving it into an ecosystem of autonomous, multi-agent systems.

We are a small, startup-minded team that ships fast and owns what we build end-to-end. We are looking for a senior SDE who lives at the application layer: designing and building the agentic workflows, LLM integrations, tool-calling systems, and AI-powered features that GRACE users interact with every day. Your focus is on what runs on top of the platform: the agents, the orchestration, the prompts, the pipelines, and the product.

The best person for this role starts with the user. They ask why before they ask how. They communicate clearly, give and receive feedback well, and make the people around them better. They are a self-starter with a high bar, a high sense of urgency, and genuine empathy for the people whose work they are making better.

What You’ll Do:

• Design and build GRACE’s core agentic workflows: multi-step reasoning, planning, memory, and tool-use across single and multi-agent systems

• Implement and evolve A2A communication patterns at the application layer, enabling GRACE agents to collaborate and hand off tasks

• Build and maintain the tool-calling layer: tool definitions, input/output schemas, error handling, retry logic, and result formatting

• Own the MCP client-side integration: how GRACE agents discover, invoke, and compose tools exposed via MCP servers

• Design multi-agent workflows that are reliable, observable, and debuggable in production, not just in demos

• Own LLM orchestration at the application layer: prompt construction, context management, model selection logic, and response parsing

• Build and maintain RAG features: query formulation, result ranking, citation grounding, and hallucination mitigation

• Implement and iterate on prompt engineering patterns and system prompts that drive GRACE’s quality and consistency across OpenAI GPT, Anthropic Claude, and Google Gemini

• Manage context window budgets: know when to truncate, summarize, or paginate, and build the logic that makes those decisions correctly

• Build evaluation pipelines for LLM quality: grounding assessment, regression testing, safety checks, and A/B experimentation on prompt and model changes

• Stay sharp on token economics: write prompts and pipelines that are cost-efficient without sacrificing output quality

• Translate ambiguous product requirements into clear technical designs and ship them fast

• Build new GRACE capabilities end-to-end: from backend application logic through to the API contract the frontend consumes

• Rapidly prototype new agentic features, run experiments, collect data, and iterate based on real user behavior

• Collaborate closely with product, UX, applied science, and operations; listen well, ask good questions, and build the right thing rather than the obvious thing

• Own the quality of what you ship: write tests, handle edge cases, and make sure your features degrade gracefully when upstream dependencies fail

• Instrument agentic workflows with tracing, logging, and metrics so failures are diagnosable and regressions are caught before users report them

• Define and monitor application-level SLOs: tool call success rates, response quality, and latency from the user’s perspective

• Build fallback and guardrail logic for AI services: what happens when a model returns something unsafe, off-topic, or structurally wrong

• Work closely with the infra engineer to understand system-level constraints and design application behavior that respects them

• Write production-quality code:…