S&C Global Network - AI - CDI -Agentic AI- Manager

Accenture in India; Gurugram District; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Accenture in India seeks an AI Decision Science Manager to lead enterprise-scale Generative AI and Agentic AI initiatives. You will define AI roadmaps, guide cross-functional teams, and partner with senior client stakeholders to implement production-grade AI platforms.

You will drive multi-agent orchestration, MCP, RAG, and cloud-native architectures while ensuring governance, security, and measurable business impact.

Qualifications

• Bachelor's or Master's degree in CS/AI/ML/IT/DS with excellent academics.
• Proven experience building enterprise AI programs and solutions.
• Strong leadership and stakeholder management skills.

Responsibilities

• Own end-to-end delivery of enterprise AI programs from strategy to production.
• Define AI architecture, roadmaps, governance, and technical standards.
• Lead multidisciplinary AI teams and mentor consultants.
• Drive delivery quality, risk management, and client satisfaction.
• Lead integration with enterprise platforms and data systems.
• Establish Responsible AI practices and governance.

Skills

Generative AI

Agentic AI Systems

Large Language Models

Model Context Protocol

Multi-Agent Systems

AI Agent Orchestration

Python

SQL

Machine Learning

LangGraph

AI Refinery

Azure OpenAI

Retrieval-Augmented Generation

Prompt Engineering

Function Calling

Tool Calling

REST APIs

LLM Fine-tuning

Enterprise AI Architecture

Solution Architecture

Education

Bachelor's or Master's degree in CS/AI/ML/IT/DS

Tools

LangChain

Azure OpenAI

Azure AI Foundry

Splunk

REST APIs

LangGraph

Microsoft Graph API

Job description

AI Decision Science Manager - Updated Job Description

Job Title

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

Management Level

7 – Manager

Location

Open

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

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.

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.