Ind & Func AI Decision Science Manager – Agentic AI & Enterprise Intelligence
7 – Manager
Open
Generative AI, Agentic AI Systems, Large Language Models (LLMs), Model Context Protocol (MCP), Multi-Agent Systems, AI Agent Orchestration, Python, SQL, Machine Learning, LangGraph, AI Refinery, Azure AI Foundry, Azure OpenAI, Retrieval-Augmented Generation (RAG), Prompt Engineering, Function Calling, Tool Calling, REST APIs, LLM Fine-tuning, AI Model Evaluation, Enterprise AI Architecture, Solution Architecture
LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, Azure AI Search, Azure Functions, Microsoft Graph API, ServiceNow Integration, Splunk, Microsoft Teams Integration, Vector Databases, Docker, Kubernetes, Azure DevOps, CI/CD, MLOps, AI Guardrails, Responsible AI, AI Evaluation Frameworks, Observability & Monitoring, Git, Cloud Platforms (Azure, AWS, GCP)
Minimum 7 years of experience in AI/ML with demonstrated expertise leading enterprise Generative AI and Agentic AI programs, managing delivery teams, solution architecture, and client engagements.
Bachelor's or Master's degree (BE/BTech/MTech/MS/MBA) in Computer Science, Artificial Intelligence, Machine Learning, Information Technology, Data Science, Mathematics, Statistics, or related disciplines with an excellent academic record.
As an AI Decision Science Manager, you will lead the strategy, architecture, delivery, and adoption of enterprise-scale Generative AI and Agentic AI solutions. You will define AI roadmaps, lead cross-functional delivery teams, engage with senior client stakeholders, and oversee the successful implementation of production-grade AI platforms. You will drive enterprise AI transformation by combining LLMs, Multi-Agent Systems, MCP, RAG, and cloud-native architectures with enterprise integrations including ServiceNow, Microsoft Graph, Teams, Splunk, Azure AI Services, and business applications.
Strategic Leadership & Delivery
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Own end-to-end delivery of enterprise AI programs from strategy through production deployment.
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Define AI architecture, delivery roadmap, governance, and technical standards.
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Lead multidisciplinary AI engineering teams and mentor consultants and analysts.
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Drive delivery excellence, quality, risk management, and client satisfaction.
Client & Stakeholder Engagement
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Act as trusted advisor to executive stakeholders on AI strategy and adoption.
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Lead workshops, solutioning sessions, architecture reviews, and executive presentations.
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Support business development, proposals, PoCs, and AI transformation initiatives.
Agentic AI & LLM Engineering
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Lead development of enterprise AI agents using AI Refinery, LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, and custom architectures.
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Oversee LLM fine-tuning, evaluation, prompt engineering, MCP implementation, and multi-agent orchestration.
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Drive reusable AI frameworks, accelerators, and enterprise standards.
Enterprise AI Integration
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Lead integrations with ServiceNow, Microsoft Graph API, Microsoft Teams, Splunk, Azure Functions, Azure SQL, and REST APIs.
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Define enterprise integration, security, authentication, and observability standards.
AI Governance & Quality
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Establish Responsible AI, guardrails, evaluation frameworks, security, compliance, and operational governance.
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Monitor AI quality, business KPIs, costs, latency, hallucinations, and production health.
Innovation & Capability Development
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Drive AI innovation, capability building, reusable assets, mentoring, and knowledge sharing.
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Stay current with emerging AI technologies and define adoption strategy.
Generative AI, Agentic AI Systems, Large Language Models (LLMs), Multi-Agent Systems, Model Context Protocol (MCP), AI Agent Orchestration, Python, SQL, Machine Learning, LangGraph, AI Refinery, LangChain, Prompt Engineering, Function Calling, Tool Calling, Retrieval-Augmented Generation (RAG), LLM Fine-tuning, AI Model Evaluation, Enterprise AI Architecture, Solution Architecture
Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Functions, Azure DevOps, Docker, Kubernetes, CI/CD, MLOps, Git
ServiceNow, Microsoft Graph API, Microsoft Teams, Splunk, Azure SQL, Vector Databases, Semantic Search, Enterprise API Integration, MCP Servers, AI Guardrails, AI Observability, Human-in-the-Loop (HITL)
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Proven experience leading enterprise AI and Agentic AI delivery programs.
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Strong executive stakeholder management and consulting experience.
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Experience defining enterprise AI architecture, governance, and operating models.
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Experience managing large cross-functional delivery teams.
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Strong understanding of Responsible AI, AI security, compliance, and enterprise-scale deployments.
