● Build and ship GenAI applications: Develop, deploy, and maintain production-grade GenAI-powered systems (e.g., RAG, agents, copilots, content intelligence, workflow automation).
● Design and deliver agentic systems: Architect and implement agentic workflows (tool use/function calling, planning, memory, reflection/verification loops, multi-agent coordination) to automate complex tasks with reliability and guardrails.
● Own architecture & scalability: Design reliable, secure, cost-efficient architectures that scale (batch + real-time), including latency optimization and throughput planning.
● Model + tooling expertise: Select and integrate appropriate LLMs and GenAI tools (open-source and/or hosted), including embeddings, re-rankers, multimodal models, and prompt/agent frameworks.
● Evaluation & quality: Create evaluation harnesses for accuracy, relevancy, safety, and hallucination reduction; implement offline/online testing and A/B experimentation.
● Data & retrieval systems: Design retrieval strategies, build indexing pipelines, optimize chunking, metadata strategies, vector databases/search, and caching.
● LLMOps / MLOps: Implement CI/CD for AI systems, model/version management, prompt and configuration management, automated testing, monitoring, alerting, and rollback strategies
● Security & governance: Apply best practices for data privacy, PII handling, access controls, audit logging, content filtering, and policy compliance.
● Mentor and lead: Coach junior engineers through code reviews, pairing, and technical design; establish patterns and standards for AI engineering.
● Collaborate and influence: Partner with Product, Design, Data, and Platform teams to define requirements, milestones, and success metrics.
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