AI Engineer Generative AI, Copilot & Agentic Solutions

Experience

6–10 years

Location

Bengaluru

Role Overview

We are seeking an experienced AI Engineer with a strong software engineering background and hands-on expertise in Generative AI, Microsoft Copilot, MCP (Model Context Protocol), AI Skills development, and AI-powered application development. The ideal candidate will have deep understanding of the Software Development Lifecycle (SDLC) and experience building, integrating, and deploying enterprise-grade AI solutions using modern development tools including VS Code, GitHub, Azure, and Microsoft AI ecosystems.

This role requires a combination of AI engineering, software architecture, and product delivery experience to design and implement intelligent copilots, agents, custom skills, and AI-driven workflows.

Key Responsibilities

AI Solution Development

- Design, develop, and deploy enterprise AI applications using LLMs and GenAI technologies.
- Build and maintain custom AI Skills, Agents, Plugins, and Tools for business-specific use cases.
- Develop AI-powered copilots and conversational experiences using Microsoft Copilot technologies.
- Implement Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge sources.
- Design prompt engineering, orchestration, and grounding strategies for AI systems.

MCP & Agentic AI

- Build and integrate MCP (Model Context Protocol) servers and tools.
- Develop agent-based workflows enabling AI systems to interact with enterprise applications and services.
- Create reusable AI capabilities and toolsets for internal development teams.




- Implement secure and scalable AI integrations across enterprise environments.

Software Engineering

- Apply software engineering best practices across the SDLC.
- Design scalable APIs, microservices, and backend components supporting AI workloads.
- Collaborate with product managers, architects, and engineering teams to define technical solutions.
- Conduct code reviews and mentor junior engineers.

DevOps & Deployment

- Implement CI/CD pipelines for AI applications.
- Monitor AI performance, reliability, security, and governance.
- Optimize application performance, cost, and scalability.
- Support production deployments and troubleshooting.

Required Qualifications

Experience

- 6–10 years of software development experience.
- 3+ years of hands-on experience in AI/ML or Generative AI development.
- Experience delivering production-grade AI applications.

Technical Skills

- Robust programming skills in Python, C#, JavaScript, or TypeScript.
- Experience with Large Language Models (LLMs) and GenAI frameworks.
- Hands-on experience building Custom AI Skills, Agents, Plugins, or Extensions.
- Knowledge of Model Context Protocol (MCP) architecture and implementation.
- Experience with Microsoft Copilot, Copilot Studio, or Copilot extensibility.
- Strong understanding of RAG architectures, vector databases, embeddings,



and semantic search.
- Experience with REST APIs, microservices, and cloud-native architectures.
- Proficiency in Visual Studio Code (VS Code) and modern development workflows.
- Experience with Git, GitHub, and CI/CD pipelines.

SDLC & Architecture

- Strong understanding of SDLC methodologies including Agile/Scrum.
- Experience with software design patterns, architecture reviews, testing, and deployment processes.
- Knowledge of secure coding practices and enterprise governance requirements.

Preferred Qualifications

- Experience with Microsoft Azure AI services.
- Knowledge of Azure OpenAI Service, AI Foundry, Cognitive Search, and Azure Functions.
- Experience with LangChain, Semantic Kernel, AutoGen, CrewAI, or similar frameworks.
- Familiarity with Microsoft Graph, Power Platform, and enterprise integrations.
- AI certification(s) from Microsoft or equivalent platforms.
- Experience in developing multi-agent systems and workflow automation solutions.

Desired Competencies

- Strong problem-solving and analytical skills.
- Excellent communication and stakeholder management abilities.
- Ability to translate business requirements into AI-driven solutions.
- Passion for emerging AI technologies and continuous learning.
- Leadership and mentoring capabilities.

Success Indicators

- Delivery of scalable AI copilots and agentic solutions.
- Creation of reusable custom AI skills and MCP integrations.
- Successful deployment of enterprise-grade AI applications.
- Improvement in developer productivity and business process automation through AI adoption.

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