Senior AI Engineer

Your work will include:

Designing, building, and hardening LLM agents with real tool use and multi-step orchestration for data quality, catalog, and governance tasks.

Owning agent reliability end to end — latency, correctness, and cost — including evaluation harnesses and production observability.

Designing and evolving our agent tooling layer, including MCP servers and protocol-level integrations, so capabilities can be exposed cleanly and securely.

Building and tuning RAG pipelines and grounding strategies where agents need reliable access to customer data and metadata.

Working across our LLM gateway (LiteLLM proxy) and model providers to route, monitor, and optimize inference.

Setting technical standards for how we build agents, and raising the bar for the whole team through design reviews and pragmatic architectural trade-offs.

What we're looking for

Strong, modern Python and a track record of shipping LLM agents to production — not just prototypes or demos.

Hands-on experience with agent architectures: tool calling, orchestration, and the failure modes that come with them.

Practical experience with MCP (or comparable protocol/tooling design), and the judgment to know when a protocol layer is worth it.

Experience building agent evaluation and observability — you measure quality and latency, you don't guess at them.

Familiarity with RAG, retrieval, and grounding techniques and their real-world limitations.

Comfort with LLM gateways/proxies (e.g. LiteLLM) and provider APIs (Azure AI Foundry, OpenAI, or similar).

Healthy skepticism toward hype — you surface trade-offs before recommending a direction.

Nice to have

Kubernetes, AWS, or Azure experience.

Background in data management, data quality, or governance.

Exposure to OAuth2/SSO and multi-tenant service design.