The Role

• Design and build GenAI systems that turn LLMs into composable, dependable tools leveraging retrieval, tool use, agentic reasoning, and structured outputs.
• Design and implement a highly reliable and scalable agent runtime: orchestration, shared state and memory, tool-calling interfaces, and scheduling for cost, latency, and quality.
• Build secure, sandboxed execution for agent actions and code; optimize cold start, isolation, and observability.
• Ship unified interfaces for multiple model sizes and providers; integrate with open tool ecosystems such as MCP-style connectors for data and actions.
• Develop an evaluation platform for online and offline assessments, A/B tests, safety checks, and regression gates that improve agent reliability over time.
• Partner with Research to deliver new agent capabilities end to end—from prototype to production.

What You’ll Bring

• Experience building agent applications with tool-calling, context engineering, or open connector integrations.
• Fluency with LLM APIs, prompting strategies, and orchestration patterns (e.g., LangChain, LlamaIndex, or custom pipelines).
• Experience with retrieval systems (e.g., semantic and lexical retrieval, vector DBs, efficient kNN), function calling, tool use, or agentic workflows.
• Strong coding skills in one or more of: Python, Java, Go. Comfortable with service design, APIs, and data models for high-throughput systems.
• Working knowledge of containers and Kubernetes concepts. Familiarity with metrics, tracing, on-call rotations, and incident response practices.
• Self-motivated with a willingness to take ownership.
• Strong communication skills and ability to work collaboratively in a team environment.

Bonus Points If…

• Prior work on agent orchestration pipelines: task routing, planning, memory graphs, vector search, or browser automation.
• Experience with evaluations, preference optimization, or RL to improve LLM reliability.