Agentic AI and LLM Applications 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 at scale. GRACE 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: readable, tested, reviewed, and documented

Communicate technical decisions clearly to both engineers and non-engineers; no one should have to guess what you decided or why

Participate actively in design reviews; push back when something is over-engineered or under-specified

Mentor and unblock other engineers; bias toward ownership and fast iteration

Ensure strong privacy, security, and compliance in all application logic and data handling

Join us. The world can’t wait.

You Have:

7+ years of experience with software engineering, including building and operating production systems

Experience in high-velocity environments where you owned and shipped complex products end-to-end

Experience in Python and at least one other backend language

Experience building and operating systems on major cloud platforms, including AWS, GCP, or Azure

Experience with containerization and working within CI/CD pipelines

Knowledge of modern backend frameworks, async patterns, distributed systems, APIs, data pipelines, and software design patterns

Ability to be a clear, direct communicator who gives and receives feedback well, works with empathy, and makes the people around them better

Ability to be a self-starter with a high bar and high sense of urgency, including not waiting to be told what to do next

Bachelor's degree in Computer Science or Software Engineering

Nice If You Have:

Experience building production systems on top of LLMs, including tool-calling, RAG, multi-step reasoning, and context management

Experience with multi-agent (A2A) architectures and orchestration frameworks in production, not just in prototypes

Experience building LLM evaluation and regression testing pipelines

Experience in startup or early-stage environments, including 0-to-1 product building

Experience in big tech building customer-facing AI platforms or developer tools at scale

Experience in security-conscious engineering, including input validation, output sanitization, audit logging, and responsible AI guardrails

Experience in healthcare, life sciences, or other regulated domains

Knowledge MCP at the client/consumer layer, including how agents discover and invoke tools via MCP

Knowledge of token economics, including cost-per-query awareness, context budget management, and prompt efficiency

Ability to demonstrate a strong intuition for prompt engineering and LLM behavior across model families, including why Claude and GPT respond differently to the same prompt and designing for it, and demonstrate comfort with ambiguity


Skills Assessment
As part of Booz Allen’s skills first hiring process, candidates must complete the required skills assessment to ensure they meet the Basic Qualifications for this role. Candidates must complete the assessment and meet the minimum proficiency threshold to continue in the hiring process.


Compensation

At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen’s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.

Salary at Booz Allen is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $86,800.00 to $198,000.00 (a