Senior Software Development Engineer

Senior Software Development Engineer III (SDE III)
Agentic AI & LLM Applications

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 Will Own
Agentic AI Systems & Orchestration
• Design and build 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 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 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

LLM Application Development
• 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 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

Features & Product
• Translate ambiguous product requirements into clear technical designs and ship them fast
• Build new 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

Observability & Reliability
• 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

Engineering Excellence & Team
• 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

Basic Qualifications
• Bachelor's or Master's in Computer Science, Software Engineering, or related field, or equivalent practical experience
• 7+ years of professional software engineering experience building and operating production systems
• Proven experience in high-velocity environments where you owned and shipped complex 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, APIs, and software design patterns
• Experience building and operating systems on major cloud platforms (AWS, GCP, or Azure)
• Experience with containerization 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 production systems on top of LLMs: tool-calling, RAG, multi-step reasoning, and context management
• Experience with multi-agent (A2A) architectures and orchestration frameworks in production, not just in prototypes
• Familiarity with MCP at the client/consumer layer: how agents discover and invoke tools via MCP
• Strong intuition for prompt engineering and LLM behavior across model families; you know why Claude and GPT respond differently to the same prompt and you design for it
• Experience building LLM evaluation and regression testing pipelines
• Demonstrated understanding of token economics: cost-per-query awareness, context budget management, and prompt efficiency
• Track record in startup or early-stage environments: 0-to-1 product building, comfort with ambiguity, high sense of urgency
• Experience in big tech building customer-facing AI platforms or developer tools at scale
• Background in security-conscious engineering: input validation, output sanitization, audit logging, and responsible AI guardrails
• Experience in healthcare, life sciences, or other regulated domains is a plus but not required