Automation & AI Lead – UiPath Azure AI

Description Key Responsibilities Strategy & Leadership - Define and own the enterprise automation and AI strategy , balancing UiPath for RPA and Azure-native AI for intelligent solutions. - Lead end-to-end delivery — discovery, architecture, build, evaluation, deployment, and hyper care. - Establish CoE governance : intake, prioritization, reusable accelerators, LLMOps, and ROI tracking. - Build, mentor, and scale a high-performing automation/AI team across geographies. Automation Delivery - Architect and deliver enterprise RPA solutions using UiPath, Power platform . - Design attended, unattended, and hybrid bots with robust exception handling, reusable frameworks, and audit controls. - Manage Orchestrator infrastructure (queues, assets, credentials, tenants) and CI/CD for bots. - Drive adoption of Automations. AI / LLM Solutioning (Strong Knowledge Required) - Design and deliver GenAI/LLM-based solutions for document extraction, classification, summarization, decisioning, and conversational interfaces. Responsibilities - Apply prompt engineering best practices , evaluate model performance, and handle edge cases (e.g., structured data extraction, address matching, exception handling). - Build RAG (Retrieval-Augmented Generation) pipelines using vector databases (Azure AI Search, Pinecone, etc.). - Integrate AI agents into RPA workflows to enable agentic automation and human-in-the-loop scenarios. - Stay current with the evolving GenAI landscape — Azure OpenAI, Anthropic, open-source LLMs, MCP, and agent frameworks. Azure & Cloud Engineering - Architect cloud-native automation solutions leveraging Azure services : App Services, Functions, Logic Apps, Service Bus, Key Vault, Storage, API Management, and Azure DevOps. - Deploy and manage Azure OpenAI workloads (GPT-4o, GPT-5 series, embeddings) with secure prompt engineering, RAG patterns, and content safety. - Implement CI/CD pipelines for bots and AI components using Azure DevOps / GitHub Actions. - Ensure cloud cost optimization, governance, and adherence to enterprise security standards. SAP & Enterprise Integration (Mandatory) - Lead automation and AI integrations with SAP S/4HANA (SD, MM, FI/CO, PP, WM/EWM) via BAPI/RFC, OData, IDoc, CPI, and Fiori. - Integrate with Salesforce, Snowflake, ServiceNow, SharePoint , and third-party platforms. - Architect solutions like intelligent order processing, master data automation, document understanding, and conversational copilots . Governance & Communication - Ensure audit-ready traceability — BRD → SDD → evaluation evidence → deployment artifacts. - Partner with auditors on SOX/ITGC controls, AI risk assessments, and management attestations. - Act as the single point of contact between IT, business teams, and SI/vendor partners across EMEA, APAC, and Americas. - Communicate clearly through business-friendly emails, Teams updates, and executive readouts. Required Qualifications – Automation & AI Lead Qualifications ✅ Experience - 10+ years in IT, with 5+ years leading automation/AI initiatives at enterprise scale. - Proven end-to-end delivery ownership - discovery through hypercare for multiple enterprise programs. - Experience establishing/scaling an Automation or AI CoE (intake, governance, accelerators, ROI tracking). - Track record of building and mentoring cross-geography teams . - Mandatory hands-on UiPath delivery — at least one enterprise-scale program with Orchestrator administration and Document Understanding / AI Center. - Mandatory hands-on Azure AI delivery — production GenAI/LLM solutions using Azure OpenAI, AI Foundry, AI Search, and Document Intelligence. - Mandatory SAP integration experience , including at least one S/4HANA program. - Experience in audit/compliance-driven environments (SOX, ITGC, or equivalent). ️ Technical Skills - UiPath: Studio, Orchestrator, REFramework, Document Understanding, AI Center, Action Center. - Power Platform: Power Automate (cloud + desktop), Power Apps, Copilot Studio. - Strong in attended, unattended, and hybrid bot design , exception handling, and CI/CD for bots. - Solid grounding in GenAI fundamentals : embeddings, tokenization, fine-tuning vs RAG, evaluation. - Advanced prompt engineering : structured outputs, function/tool calling, guardrails, edge-case handling. - Hands-on RAG pipelines with vector stores ( Azure AI Search, Pinecone ) — chunking, hybrid search, re-ranking. - Experience with agentic frameworks : Semantic Kernel, LangChain, AutoGen, Azure AI Agent Service, MCP . - Azure AI: Azure OpenAI (GPT-4o, GPT-5, embeddings), AI Foundry, AI Search, Document Intelligence, Content Safety. - DevOps & LLMOps: Azure DevOps, GitHub Actions, CI/CD, prompt versioning, observability, cost optimization. Soft Skills - Exceptional communication — translates AI/automation concepts for executive and business audiences. - Strong stakeholder management across IT, business, and SI/vendor partners (EMEA, APAC, Americas). - Highly detail-oriented and analytical , with a bias for traceability and governance. - Comfortable navigating ambiguity and driving adoption of industry best practices .