What We Require

Non-negotiable

Demonstrated experience building and shipping AI agent systems in production: not demos, not internal tools that never went live

Ownership of an evaluation pipeline for a production AI system: you defined the metrics, built the framework, and used it to make deployment decisions

Experience debugging production AI failures: you have traced a silent agent degradation to its root cause in a live system

Proficiency in Python and LLM frameworks (LangChain, LlamaIndex, or equivalent)

RAG architecture: retrieval pipeline design, vector database implementation, chunking and embedding strategy

API design and backend integration at production scale

Secure tool access patterns for agentic systems: you know where LLM reasoning ends and deterministic enforcement must begin, and you have built the boundary between them

Experience implementing guardrails: input validation, output filtering, and execution-layer prompt injection defence

CI/CD for agentic systems: you have implemented progressive delivery pipelines for agents, including instrumentation of tool invocations and decision points, staging with regression benchmarks, and canary deployment to detect behavioural drift before full rollout

Strong Advantage

Experience in regulated financial services, insurance, or healthcare environments

Familiarity with graph-based agentic orchestration frameworks (LangGraph or equivalent), particularly durable execution and human-in-the-loop checkpointing in regulated environments

Familiarity with Azure AI tooling and services

MLOps practices: monitoring, observability, cost management for inference workloads

CRM and enterprise system integration

Educational Requirements

Bachelor's degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Data Science, or a related field

OR equivalent practical industry experience building and deploying production-grade AI systems