GenAI Engineer / AI Agent Engineer – AI Foundry & Copilot Studio

• Design, build, and deploy AI agents using Foundry + Studio to solve real business problems
• Implement core agent capabilities:
• Tool/function calling, multi-step planning, task decomposition
• Retrieval-Augmented Generation (RAG) with enterprise knowledge sources
• Memory patterns (session/state, long-term memory with governance)
• Guardrails (policy checks, prompt safety, structured outputs)
• Develop agent workflows integrating:
• APIs, databases, event-driven services, internal tools
• Approval loops, HITL (Human-in-the-Loop), and escalation handling

• Build and optimize RAG pipelines:
• Document ingestion, chunking, embedding strategies
• Vector search
• Grounding, citations, and source traceability
• Connect enterprise data sources in AI Foundry (datasets, ontology/semantic models where applicable) and operationalize for agent usage.

• Create evaluation frameworks for GenAI:
• Automated tests for factuality/grounding, relevance, toxicity, refusal correctness
• Offline + online evaluation, regression testing for prompts and agent tools
• Implement observability:
• Agent traces, tool-call logs, latency/cost metrics, failure modes
• Ensure security, compliance, and governance:
• Access control, secrets management, PII handling
• Model usage policies, auditability, and change management

• Partner with stakeholders to translate requirements into agent designs and deliver measurable outcomes.
• Contribute to reusable libraries, templates, and best practices for Foundry/Studio agent development.

• 3–5 years in software engineering (Python/Java/TypeScript or similar) with production deployment experience.
• Hands-on experience building AI agents in AI Foundry and Copilot Studio (agent workflows, tool integration, deployment).
• Strong understanding of LLMs and prompting patterns:
• System prompts, structured outputs (JSON), function/tool calling, chain-of-thought-safe patterns
• Solid experience with RAG and search:
• Embeddings, vector databases/search, chunking, reranking, grounding techniques
• Experience integrating GenAI solutions with:
• REST APIs, microservices, message queues, databases
• Familiarity with software engineering best practices:
• Unit/integration testing, CI/CD, code reviews, documentation

• Experience with one or more agent frameworks/concepts:
• LangGraph/LangChain, Semantic Kernel, AutoGen-style orchestration patterns
• Experience with model providers and deployment patterns:
• Azure OpenAI / OpenAI / open-source models
• Exposure to governance and compliance in enterprise AI:
• Data classification, audit logging, model risk management

• GenAI/LLM: Prompt engineering, tool calling, structured outputs, safety patterns
• AI Foundry & Copilot Studio: Building pipelines, deploying apps/workflows/agents, access controls
• Agents: Planning + tools + memory + orchestration; multi-agent (optional)
• RAG: Embeddings, retrieval strategies, reranking, grounding/citations
• Evaluation: Golden datasets, automated evals, human review loops, regression testing
• MLOps/DevOps: CI/CD, containerization, monitoring, performance/cost optimization

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