Our Client Currently looking for Sr Staff AI Engineer

Your core focus areas:

• Agentic architecture. Design and ship multi-agent systems — orchestration, tool use, planning loops, memory, and failure recovery — that operate reliably in production, not in notebooks.
• Enterprise-grounded reasoning. Build agents that leverage Access Graph, Access Reviews, and permission and risk data — to make decisions with context no frontier model has on its own.
• Trust, safety, and governance. Own the guardrails: observability, human-in-the-loop controls, and compliance infrastructure that make autonomous systems safe to deploy at scale.
• Retrieval and grounding. Work closely with our different product teams, platform and graph teams to ensure agents are grounded in accurate, low-latency retrieval — RAG pipelines, semantic search, re-ranking, and evaluation — as a critical dependency of agentic quality.
• Model integration and evaluation. Integrate frontier models, evaluate trade-offs across cost, latency, and capability for production use cases.
• Engineering leadership. Raise the technical bar through architecture decisions, code reviews, and coaching — particularly on agentic design patterns and production AI discipline.

Qualifications

Qualifications

To be successful in this role you have:

• 8+ years of software engineering with strong fundamentals in data structures, algorithms, and distributed systems.
• Hands-on depth designing, shipping, and operating agentic systems in production — multi-agent orchestration, tool calling, planning loops, memory, and failure recovery. Not prototypes.
• Production-grade Python. Systems language (Go, Java, or C++) is a plus.
• Working experience with frontier AI SDKs (Anthropic, Google, or OpenAI) — prompt engineering, structured outputs, and model evaluation in production settings.
• Familiarity with RAG and retrieval patterns in production — vector stores, hybrid search, and retrieval evaluation metrics.
• Track record of technical leadership: architecture ownership, code quality bar-raising, and mentoring engineers on production AI practices.

Nice to Have

• Deeper specialization in search and retrieval at scale or MLOps/model observability.
• Published work or open-source contributions in agentic systems or retrieval.
• Exposure to LLM fine-tuning or inference optimization in production.

• Are you interested in this position?

   

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