• Partner with stakeholders to identify, evaluate, document, and shape GenAI use cases (copilots, automation, decision support, and insight generation) with clear success metrics. • Design solution architectures that integrate LLMs with enterprise systems, data sources, and tool/function calling while meeting latency and reliability expectations. • Develop prototypes rapidly and validate them through evaluation, red-teaming, and user feedback; document tradeoffs and recommendations. • Build production-grade services and full-stack experiences (APIs, UIs, workflows) with secure authentication/authorization, audit logging, and scalable deployment patterns. • Implement safety, privacy, and compliance controls (e.g., PHI/PII protection, prompt injection defenses, data residency constraints, and policy-based filtering). • Instrument solutions end-to-end with metrics, traces, logs, and model/app observability; contribute to SLOs, error budgets, and operational runbooks. • Build and maintain evaluation harnesses for LLM quality, safety, and business outcomes (offline tests, golden sets, regression suites, and online experiments). • Implement RAG pipelines (chunking, embedding, vector search, reranking) and optimize for accuracy, cost, and latency. • Collaborate with platform teams on deployment, monitoring, drift/quality detection, and incident response for model-backed services. • Contribute reusable libraries and patterns for prompt management, retrieval, tool calling, and policy enforcement. • Participate in design reviews and code reviews; mentor senior and mid-level engineers on GenAI engineering practices. • Continuously improve developer experience through templates, CI/CD automation, and documentation that accelerates safe adoption.