Role : AI Decision Science Consultant & Manager - Role

Job Title :

Ind & Func AI Decision Science Manager - Agentic AI & Enterprise Intelligence

Management Level : Consultant & Manager

Must Have Skills :

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

Good to Have Skills :

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)

Experience :

4 - 12 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.

Educational Qualification :

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.

Job Summary :

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.

Roles & Responsibilities :

Strategic Leadership & Delivery :

- Own end-to-end delivery of enterprise AI programs from strategy through production deployment.

- Define AI architecture, delivery roadmap, governance, and technical standards.

- Lead multidisciplinary AI engineering teams and mentor consultants and analysts.

- Drive delivery excellence, quality, risk management, and client satisfaction.

Client & Stakeholder Engagement :

- Act as trusted advisor to executive stakeholders on AI strategy and adoption.

- Lead workshops, solutioning sessions, architecture reviews, and executive presentations.

- Support business development, proposals, PoCs, and AI transformation initiatives.

Agentic AI & LLM Engineering :

- Lead development of enterprise AI agents using AI Refinery, LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, and custom architectures.

- Oversee LLM fine-tuning, evaluation, prompt engineering, MCP implementation, and multi-agent orchestration.

- Drive reusable AI frameworks, accelerators, and enterprise standards.

Enterprise AI Integration :

- Lead integrations with ServiceNow, Microsoft Graph API, Microsoft Teams, Splunk, Azure Functions, Azure SQL, and REST APIs.

- Define enterprise integration, security, authentication, and observability standards.

AI Governance & Quality :

- Establish Responsible AI, guardrails, evaluation frameworks, security, compliance, and operational governance.

- Monitor AI quality, business KPIs, costs, latency, hallucinations, and production health.

Innovation & Capability Development :

- Drive AI innovation, capability building, reusable assets, mentoring, and knowledge sharing.

- Stay current with emerging AI technologies and define adoption strategy.

Professional & Technical Skills :

Must Have :

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

Cloud & Infrastructure :

Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Functions, Azure DevOps, Docker, Kubernetes, CI/CD, MLOps, Git

Enterprise AI Technologies :

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)

Preferred Qualifications :

- Proven experience leading enterprise AI and Agentic AI delivery programs.

- Strong executive stakeholder management and consulting experience.

- Experience defining enterprise AI architecture, governance, and operating models.

- Experience managing large cross-functional delivery teams.

- Strong understanding of Responsible AI, AI security, compliance, and enterprise-scale deployments.