Platform Engineer

• Build the Central AI Platform: Design and build a unified, resilient platform for deploying and serving AI features, including routing layer with provider fallbacks, circuit breakers, and cost/latency-aware model selection, central registry for versioning models and prompts, and robust CI/CD pipelines.
• Architect for Scale and Quality: Own end-to-end Retrieval-Augmented Generation (RAG) strategy; lead design of embedding pipelines, develop chunking strategies, implement hybrid search, manage index maintenance, and build/scale LLM evaluation tooling (golden sets, rubric-based scoring, LLM-as-judge with bias controls).
• Ensure Production Excellence: Instrument AI systems with deep observability (structured tracing, cost-attribution, latency metrics); define and uphold SLOs; create incident response runbooks; build safety guardrails for mission-critical AI services.
• Partnership with Infrastructure: Own LLM runtime, retrieval architecture (vector stores, indexing), evaluation frameworks, safety guardrails, prompt/model versioning, AI observability, and cost/latency optimization while collaborating with Infrastructure on cloud, networking, secrets, Kubernetes/GPU orchestration, and shared platform services.
• Cross-team collaboration: Act as force multiplier for product and AI teams to ship features faster, safer, and smarter; make strategic build-vs-buy decisions and influence rollout strategies, SLAs/SLOs, incident response, and capacity planning.

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