Gen AI Fullstack - Senior Engineer

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.

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• 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.