AI Engineer II

Job highlights

• familiarity with MCP and tool ecosystems is preferred
• Generative AI / LLMs: Hands-on experience with major model APIs,structured outputs,tool / function calling,model selection,and prompt / context design
• Frameworks: Practical experience with LangGraph,LangChain,LlamaIndex,or equivalent orchestration libraries

What Youll Do
Independently design, build, deploy, and operate AI-powered features, APIs, services, and workflows.
Develop production LLM applications using structured outputs, tool/function calling, RAG, agentic workflows, and context-management patterns.
Design retrieval pipelines across structured and unstructured enterprise data, including chunking, embeddings, vector/hybrid search, ranking, and grounding strategies.
Evaluate and integrate models from multiple providers based on quality, latency, reliability, security, and cost trade-offs.
Build reusable components within your pods codebase for prompts, tools, retrieval, and evaluations, and flag candidates for cross-pod standardization.
Define and maintain automated evaluations and regression tests for AI quality, groundedness, task success, and failure modes.
Implement production controls including observability, caching, retries, fallbacks, rate limits, human-in-the-loop flows, and cost controls.
Troubleshoot production issues and improve reliability, latency, quality, and operating cost using measurable signals.
Participate in technical design reviews and contribute to AI engineering standards and documentation.
Support and mentor AI Engineer I teammates through reviews, pairing, and knowledge sharing.

Technical Skills & Technology Stack
Programming: Strong Python and software engineering fundamentals; working knowledge of Java or JavaScript/TypeScript is useful for platform integration.
Data: Strong SQL; Snowflake or equivalent cloud data platforms; structured and unstructured data processing.
Generative AI / LLMs: Hands-on experience with major model APIs, structured outputs, tool/function calling, model selection, and prompt/context design.
AI Application Development: RAG, embeddings, vector/hybrid search, retrieval/ranking, agentic workflows, context management, and basic memory patterns.
Frameworks: Practical experience with LangGraph, LangChain, LlamaIndex, or equivalent orchestration libraries; ability to work without framework lock-in.
APIs & Integration: REST APIs, JSON, asynchronous/service integration patterns; familiarity with MCP and tool ecosystems is preferred.
Cloud & Engineering: AWS or equivalent cloud, Docker, Git, CI/CD, automated testing, logging, and production debugging.
Production AI: Evaluations, observability/tracing, guardrails, caching, fallbacks, latency optimization, and token/cost management.

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