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Mid-level AI role, moderate brand, niche platform skills reduce applicant density.
Highly domain-specific LLM, agent, RAG, and Foundry skills limit cross-industry transferability.
Requires 3–5 years plus hands-on Foundry/Copilot and RAG experience, making filters stringent.
Design, build, and deploy AI agents using AI Foundry and Copilot Studio to solve business problems, including implementing capabilities like multi-step planning, tool/function calling, and memory patterns.
Develop and optimize retrieval-augmented generation (RAG) pipelines and integrate enterprise data sources for AI agent usage.
Ensure production readiness by creating evaluation frameworks, implementing observability, and maintaining security, compliance, and governance for deployed AI solutions.
3–5 years of software engineering experience with production deployment, using Python/Java/TypeScript or similar.
Hands-on experience building AI agents in AI Foundry and Copilot Studio with agent workflows and tool integration.
Strong knowledge of LLMs, prompting patterns, RAG techniques (embeddings, vector search, grounding).
Experience integrating GenAI solutions with REST APIs, microservices, message queues, and databases.
Experienced in end-to-end AI agent lifecycle from design through deployment in enterprise environments using Foundry and Studio.
Strong software engineering discipline emphasizing testing, CI/CD, documentation, and production monitoring.
Deep understanding of complex GenAI workflows involving multi-step task planning, stateful memory, and compliance/governance requirements.