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

Key Responsibilities

AI Agents & GenAI Solution Development

• 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

Data & Knowledge Integration

• 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.

Engineering Excellence (Production Readiness)

• 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

Collaboration & Delivery

• 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.

Required Qualifications (Must-Have)

• 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

Preferred Qualifications (Nice-to-Have)

• 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

Core Technical Skills (Current GenAI Stack)

• Languages: Python (preferred), Java/TypeScript (plus)
• 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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