• Deliver internal copilots and customer/agent-facing automations with clear SLAs, rollbacks, and observability from day one. • Design ingestion, chunking, embeddings, indexing, hybrid search/rerank, and retrieval evaluation; track retriever quality via offline golden sets and online metrics. • Design and implement scalable AWS architectures, including AWS AI features such as Bedrock, IAM, knowledge bases, secure secrets and policy enforcement, automated provisioning, and resource-usage governance as core platform capabilities. • Add tracing, prompt/agent version lineage, eval dashboards, and regression alerts; establish golden datasets and canary tests. • Enforce PII redaction, safety filters, role-based access, audit logs, and human-in-the-loop review paths to control quality and risk. • Version and deploy prompts, tools, agents, and retrieval pipelines; support blue/green and shadow deploys with automatic rollback triggers. • Cut run-rate spend through caching, truncation, batching, autoscaling, and model routing; establish clear unit economics per workflow. • Provide templates, SDKs, and high-quality abstractions that let product teams ship safely without bespoke plumbing; improve developer experience. • Build primarily in Python and Metaflow (Outerbounds); deploy on AWS (Bedrock + core services) and OpenAI; use Cursor in daily workflows; help evaluate and, when appropriate, run on Databricks. • Participate in on-call, author runbooks, and remove single-thread risk for AI services; drive reliability and resilience akin to ML Ops.