Senior Software Development Engineer, Agentic AI & LLM Applications

Role Overview

• Support agentic AI systems and orchestration, LLM application development, features and products, observability and reliability, and engineering excellence
• 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, and build and maintain the tool-calling layer, including tool definitions, input and output schemas, error handling, retry logic, and result formatting
• Own the MCP client-side integration, including 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
• Own LLM orchestration at the application layer, including prompt construction, context management, model selection logic, and response parsing
• Build and maintain RAG features, including 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 and know when to truncate, summarize, or paginate, and build the logic that makes those decisions correctly
• Build evaluation pipelines for LLM quality, including grounding assessment, regression testing, safety checks, and A/B experimentation on prompt and model changes
• Stay sharp on token economics and 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 product capabilities end-to-end, including from backend application logic through to the API contract the frontend consumes, and 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, write tests, handle edge cases, and make sure 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, including what happens when a model returns something unsafe, off-topic, or structurally wrong, and 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, and push back when something is over-engineered or under-specified
• Ensure strong privacy, security, and compliance in all application logic and data handling

Requirements

• 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 with at least 2 backend languages, including Python
• Experience building and operating systems on major cloud platforms, such as AWS, GCP, or Azure
• Experience with containerization and working within CI/CD pipelines
• Knowledge of modern backend frameworks and async patterns
• Knowledge of algorithms, data structures, APIs, and software design patterns
• Bachelor's degree in Computer Science or Software Engineering

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform
• Python

Benefits

• health, life, disability, financial, and retirement benefits
• paid leave
• professional development
• tuition assistance
• work-life programs
• dependent care
• recognition awards program for exceptional performance