Lead Data Scientist

Job Description

Mandatory Skills: Advanced GenAI & Agentic Framework Concepts, Cloud Application Integration & Deployment, Agentic AI Systems, Retrieval-Augmented Generation (RAG), Azure OpenAI Service, AI Agents & Tool Calling, LangChain

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.

Mandatory Competencies