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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Agentic AI and LLM Applications Software Development Engineer - **Company:** Booz Allen Hamilton Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $86,800.0 - $198,000.0 - **Contract:** Permanent contract - **Skills:** Adobe InDesign, Application Programming Interfaces (APIs), Amazon Web Services, Application Layers, Business Logic, Audit Trail, Microsoft Azure, Cloud Computing, Data Validation, Software Design Patterns, Programming Tools, Distributed Systems, Python (Programming Language), Regression Testing, Software Engineering, Data Logging, Data Processing, Google Cloud, Large Language Models, Multi-Agent Systems, Backend, Build Management, Containerization, AI Platforms, Kubernetes, Information Technology, Front End Software Development, GPT, Data Pipelines - **Published:** August 28, 2026 - **Apply:** https://www.dice.com/job-detail/b7f94082-bc08-43e6-9ed8-d38a8eda2242 ## About the Role * 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, Google Cloud Platform, 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 ## Description 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 ## Related Videos - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Streaming AI Responses in Real-Time with SSE in Next.js & NestJS](https://www.wearedevelopers.com/videos/1630-streaming-ai-responses-in-real-time-with-sse-in-next-js-nestjs) - [Three years of putting LLMs into Software - Lessons learned](https://www.wearedevelopers.com/videos/1508-three-years-of-putting-llms-into-software-lessons-learned) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [The Prompt Engineer ✍️](https://www.wearedevelopers.com/magazine/216-the-prompt-engineer) - [13 AI Tools You Have to Try](https://www.wearedevelopers.com/magazine/219-13-ai-tools-you-have-to-try) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care)