Gen AI - Lead

Job location: Noida Overall experience: 7 to 12 years Employment type: Full Time, Permanent Job type: fulltime Job Summary Gen AI - Lead Location: Noida Company: Iris Software Job Description Mandatory Skills Agentic AI Systems Advanced GenAI Agentic Framework Concepts Cloud Application Integration Deployment FastAPI Framework Python Graph RAG Retrieval-Augmented Generation (RAG) AI Agents Tool Calling LangChain LangGraph Vector Databases AI Search Index Additional Skills PySpark Key Responsibilities Define and drive enterprise Generative AI strategy aligned with organizational objectives, innovation goals, and AI transformation initiatives. Establish AI engineering standards, governance frameworks, and best practices for enterprise AI solution delivery. Lead the design of enterprise-scale AI architecture using Amazon Bedrock, Azure OpenAI Service, Azure AI Foundry, Grog, or AI Search Index platforms. Define enterprise standards for Retrieval-Augmented Generation (RAG), Graph RAG, Agentic AI systems, and intelligent automation architectures. Drive adoption of AI agents, tool-calling frameworks, and autonomous workflow solutions across business functions. Drive adoption of enterprise multi-agent architectures, Human-in-the-Loop (HITL) governance, and scalable AI engineering patterns supporting secure and reliable business automation. Establish governance standards for model customization, fine-tuning, prompt engineering, retrieval quality, and AI solution lifecycle management. Define enterprise standards for prompt engineering pipelines, vector databases, semantic search, Retrieval-Augmented Generation, Model Context Protocol (MCP), and AI agent orchestration frameworks. Define architecture patterns and engineering standards using LangChain, LangGraph, and related workflow orchestration frameworks. Establish enterprise AI engineering standards covering secure API design, authentication, authorization, asynchronous processing, scheduling, scalability, deployment, observability, and operational resilience. Establish cloud integration and deployment standards for scalable and secure AI-enabled applications. Establish enterprise integration standards supporting databases, enterprise APIs, messaging platforms, and cloud-native AI application deployment. Lead architecture reviews and ensure AI solutions meet scalability, reliability, maintainability, explainability, and business value objectives. Guide teams on GenAI architecture, agentic systems, AI governance, and enterprise AI adoption best practices. Identify AI-related risks, governance gaps, operational challenges, and architectural limitations while defining mitigation strategies. Collaborate with various teams and leadership stakeholders to align AI initiatives with organizational objectives. Drive continuous improvement initiatives focused on AI maturity, innovation, operational effectiveness, governance, and business value realization. Behavioral Competencies Demonstrates leadership and accountability in driving AI engineering excellence across programs and initiatives. Collaborate effectively with various teams and business stakeholders to ensure smooth delivery. Promotes a culture of innovation, responsible AI adoption, quality, and continuous improvement. Applies strategic thinking to address AI risks, architectural challenges, governance requirements, and business priorities. Demonstrates strong decision-making while balancing innovation, scalability, reliability, governance, and business objectives. Communicates effectively regarding AI strategy, risks, dependencies, solution outcomes, and business value. Maintains a proactive approach toward AI governance, engineering standards, and solution excellence. Mandatory Competencies Data AI - Gen AI - Advanced GenAI Agentic Framework Concepts Data AI - Gen AI - Cloud Application Integration Deployment Data AI - Gen AI - Python Data AI - Gen AI - Retrieval Augmented Generation (RAG) / Graph RAG / Agentic AI Systems Data AI - Gen AI - AI Search Index Data AI - Gen AI - Prompt Engineering / Vector Databases Data AI - Gen AI - Fine tuning Model Customization / AI Agents Tool Calling