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

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

Job Title

Ind & Func AI Decision Science Analyst – Agentic AI & Intelligent Automation

Management Level

11 – Analyst

Location

Gurugram

Must Have Skills

Generative AI, Agentic AI Systems, Large Language Models (LLMs), Model Context Protocol (MCP), Multi-Agent Systems, AI Agent Orchestration, Python, SQL, 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 Solution Development

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 2 years of experience in AI/ML with demonstrated expertise in Generative AI, Large Language Models (LLMs), Agentic AI systems, Multi-Agent Systems, and enterprise AI application development. Experience in building and deploying production-grade AI solutions within a consulting or enterprise environment is preferred.

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 Analyst, you will design, develop, fine-tune, evaluate, and deploy enterprise-scale AI solutions powered by Large Language Models (LLMs), Agentic AI, and Multi-Agent Systems. You will build intelligent AI agents capable of reasoning, planning, collaborating, and autonomously executing complex business workflows using advanced orchestration frameworks and Model Context Protocol (MCP).

You will develop enterprise AI applications integrating with platforms such as ServiceNow, Microsoft Graph, Microsoft Teams, Splunk, Azure AI Services, Azure Functions, databases, and REST APIs to automate business processes and enhance operational efficiency. You will work closely with solution architects, product owners, engineers, and business stakeholders to deliver secure, scalable, and production-ready AI solutions across multiple industries.

Roles & Responsibilities

Advanced Generative AI & Agentic AI Development

• Design, develop, and deploy enterprise AI agents using AI Refinery, LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, and custom agentic architectures.
• Build intelligent multi-agent systems capable of reasoning, planning, task decomposition, collaboration, and autonomous execution.
• Develop reusable agent orchestration frameworks, AI accelerators, and enterprise AI components.
• Design and implement Model Context Protocol (MCP) servers and integrations enabling seamless communication between AI agents and enterprise applications.
• Design Human-in-the-Loop (HITL) workflows where business approvals or validations are required.
• Build AI copilots, autonomous agents, and workflow automation solutions for enterprise use cases.

LLM Engineering & AI Model Development

• Build, fine-tune, evaluate, and optimize Large Language Models (LLMs) for enterprise use cases.
• Develop Generative AI applications using advanced prompt engineering, structured outputs, function calling, and tool calling.
• Evaluate AI models across accuracy, latency, grounding quality, safety, hallucination rates, and business KPIs.
• Optimize prompts, agent workflows, and reasoning strategies to improve reliability and performance.
• Implement model evaluation pipelines and continuous improvement mechanisms.

Retrieval-Augmented Generation (RAG)

• Design and implement enterprise RAG solutions using Azure AI Search, vector databases, semantic search, and knowledge retrieval techniques.
• Develop document ingestion, chunking, metadata enrichment, indexing, and retrieval pipelines.
• Optimize grounding strategies and retrieval quality to improve AI response accuracy.
• Build scalable enterprise knowledge management solutions powered by LLMs.

Enterprise AI Integration

• Integrate AI agents with ServiceNow, Microsoft Graph API, Microsoft Teams, Splunk, Azure Functions, Azure SQL, and REST APIs.
• Build secure MCP integrations enabling AI agents to interact with enterprise systems.
• Develop scalable enterprise automation workflows leveraging AI agent orchestration.
• Design resilient API integrations with security, authentication, logging, retries, and error handling.

AI Governance, Evaluation & Observability

• Implement AI guardrails, Responsible AI principles, governance controls, and compliance standards.
• Design evaluation frameworks for measuring AI quality, grounding, latency, hallucination rates, and business outcomes.
• Build observability dashboards to monitor AI agent execution, token consumption, workflow performance, cost, and operational health.
• Ensure enterprise AI applications meet organizational security, governance, and compliance requirements.

Solution Engineering & Deployment

• Collaborate with architects, product owners, and engineering teams to design and deliver scalable cloud-native AI solutions.
• Deploy AI applications using Azure AI Foundry, Azure OpenAI, Azure Functions, and enterprise cloud platforms.
• Participate in architecture, design, code reviews, testing, deployment, and production support.
• Implement CI/CD pipelines and MLOps practices for AI solution lifecycle management.

Innovation & Knowledge Sharing

• Stay updated with advancements in Generative AI, Agentic AI, MCP, LLMs, and enterprise AI technologies.
• Develop reusable frameworks, accelerators, templates, and best practices.

Create technical documentation and mentor junior team members