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M365 Copilot Gen AI

M365 Copilot Gen AI

Cognizant

3 - 8 years

0 Lacs

chennai tamil nadu india

Posted:3 days ago| Platform:

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Skills Required

ai developer openai azure orchestration engineering data drive automation reasoning extensibility integration retrieval model planning management development word excel api power automate database security filtering monitoring architecture aws containerization design workflow

Work Mode

On-site

Job Type

Full Time

Job Description

Position: Gen AI & Copilot Developer

Exp : 3 to 8 Years

GenAI, Agentic AI & Copilot Developer :

with experience in building and deploying AI copilots, agentic AI solutions, and LLM-powered applications. The ideal candidate will have hands-on experience with OpenAI / Azure OpenAI, LLM orchestration frameworks (e.g., LangChain, Semantic Kernel), prompt engineering, and tool/function calling.The role involves designing intelligent, multi-step agents that integrate with enterprise data and services to drive automation, reasoning, and decision-making. Experience with Microsoft Copilot extensibility and M365 integration is highly preferred.

Foundations of Generative AI
Understanding of LLMs (GPT, Claude, LLaMA, etc.)
Differences between generative AI vs traditional ML
Prompt engineering: Zero-shot, few-shot, chain-of-thought, and retrieval-augmented generation (RAG)
Model capabilities, limitations, and hallucination handling
Agentic AI Concepts
What is an AI Agent? How does it differ from a copilot?
Multi-step reasoning, task planning, and goal decomposition
Memory, context management, and tool usage
Knowledge grounding and external action execution
Use of frameworks like LangChain, Semantic Kernel, or CrewAI
Copilot Development (Microsoft Ecosystem)
Copilot extensibility in Microsoft 365 (e.g., Word, Excel, Outlook)
Copilot Studio: building custom copilots, integrating plugins
Graph connectors and Microsoft Graph API usage
Use of Power Platform (Power Automate, Power Apps) in AI-powered solutions
LLM Orchestration & Integration
LangChain/Semantic Kernel: chains, agents, memory, tools
Tool/function calling (e.g., OpenAI tool use, plugins)
Vector database integration (Pinecone, FAISS, Azure AI Search)
Data grounding (RAG pipelines)
Security, Privacy & Governance
Handling PII and enterprise data securely in GenAI solutions
Prompt injection, model output filtering, and misuse prevention
Monitoring and observability of AI agent behavior
Architecture and Deployment
Building scalable GenAI-powered APIs and microservices
Deploying to Azure, AWS, or hybrid environments
Using containerization and API gateways for modular agent-based services
Use Case Design and Real-World Scenarios
Walkthrough of past GenAI or agent projects (internal or POCs)
Business-centric use cases: HR assistant, policy copilot, knowledge bot, workflow automation
Measuring success: accuracy, user feedback, adoption
Soft Skills & Collaboration
Collaborating with business teams to translate requirements
Balancing innovation and feasibility
Communicating AI limitations clearly to stakeholders

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