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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