Responsibilities

- 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.

Qualifications

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-protected 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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 GenAI Engineer / AI Agent Engineer (Mumbai)
🏢 DXC Technology
📍 Mumbai