Platform Engineer

• Design, build, and operate production AI systems that power platform experiences: RAG pipelines, model endpoints, evaluation harnesses, guardrails, and multi tenant AI services published through a developer hub/marketplace.
• Lead end to end AI solutioning: problem framing, data sourcing, labeling strategy, model/LLM selection, and safety considerations.
• Build retrieval augmented generation and agentic workflows: document processing, chunking/embedding strategies, vector indexing, prompt/chain design, tool use, and guardrails.
• Implement evaluation with golden datasets, automatic metrics, rubric based grading, and human in the loop reviews; run A/B and interleaving tests.
• Productionize inference (synchronous, batch, streaming) with autoscaling, caching, request routing/fallbacks, cost and token/latency budgets, and kill switch patterns.
• Establish LLMOps/MLOps foundations: experiment tracking, model registry, data/model lineage, governance, CI/CD for models/prompts, and safe rollback.
• Instrument AI observability (prompt traces, quality drift, hallucination/safety events, per tenant cost dashboards) and drive continual improvement cycles.
• Partner with platform engineers to expose AI capabilities as APIs/SDKs, publish to the marketplace with metadata, versioning, and deprecation policies.
• Collaborate with security, privacy, and risk on data minimization, access controls, PII handling, and policy aligned evaluation; document tradeoffs and decisions.
• Mentor engineers and data scientists; create reusable evaluation harnesses, prompt libraries, and templates that accelerate feature delivery.

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