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.

(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   At DXC Technology, we believe strong connections and community are key to our success. Our work model prioritizes in-person collaboration while offering flexibility to support wellbeing, productivity, individual work styles, and life circumstances. We’re committed to fostering an inclusive environment where everyone can thrive. Recruitment fraud is a scheme in which fictitious job opportunities are offered to job seekers typically through online services, such as false websites, or through unsolicited emails claiming to be from the company. These emails may request recipients to provide