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

Job Description: 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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