Gen AI - Lead

. 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.

. 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.