Responsibilities Design end-to-end agentic workflows to solve complex problems Translate business needs into structured, scalable AI solutions Build and compose reusable skills, tools, and agents Choose the right approach for performance and maintainability Integrate multiple LLMs, optimizing cost, speed, and quality Improve performance via prompt design, token use, and inference efficiency Define evaluations, metrics, and guardrails to ensure quality and reliability Structure and validate data using typed schemas Implement observability, logging, and debugging tools Leverage AI assistants to accelerate development Collaborate cross-functionally to deliver impactful AI solutions Requirements 5+ years Python development with strong software engineering fundamentals Hands‑on experience building production systems with Large Language Models High ownership & proactive communication Proven experience with LangGraph or similar agentic frameworks Strong understanding of evaluation methodologies for LLM/agent quality Proven ability to decompose complex, ambiguous business problems into scalable technical solutions Comfort using AI assistants (e.g., Claude Code) Familiarity with LLM observability tools Background with vector databases and RAG patterns Production experience with async Python patterns Experience with Azure deployment and cloud‑native architectures Knowledge of evaluation frameworks and metrics for AI output quality #J-18808-Ljbffr
