Digital and AI Transformation Evangelist

Quest Global; Pune District; Hybrid; INR 1,500,000 - 2,100,000; Full time

Job summary

Quest Global is seeking an experienced AI Evangelist to drive AI adoption across our engineering teams and customer engagements. You will act as a technology leader, mentor, and solution architect, helping both internal teams and clients deploy AI and Agentic AI solutions that deliver measurable business value.

You will establish AI engineering standards, engage with customers, and shape enterprise-scale architectures leveraging agents, RAG, copilots, and automation across platforms.

Qualifications

• Strong experience in software architecture and enterprise solution design.
• Hands-on experience with Generative AI, LLMs, AI Agents, Agentic AI frameworks, and RAG architecture.

Responsibilities

• Drive widespread adoption of AI tools and practices across engineering teams. Enable engineers to maximize productivity using tools such as GitHub Copilot, Microsoft Copilot, ChatGPT, Claude, Gemini, and emerging AI platforms.
• Act as the AI subject matter expert in customer meetings, workshops, and pre-sales discussions.
• Design and review enterprise-scale AI and Agentic AI solution architectures. Architect solutions involving AI agents, multi-agent systems, RAG, copilots, workflow automation, and intelligent decision-making systems.
• Establish AI engineering standards, reusable frameworks and solutions.

Skills

AI Architecture

LLMs & Agents

Python

Cloud Platforms

Client Engagement

AI Governance

MLOps knowledge

Tools

Azure OpenAI

OpenAI

Anthropic

Gemini

AWS Bedrock

Job description

Job Requirements

Experience

15+ Years Overall Experience with 7+ Years in AI/ML, Generative AI, Intelligent Automation, or AI Solution Architecture.

Role Overview

We are looking for an experienced AI Evangelist to drive AI adoption across our engineering organization and customer engagements. The AI Evangelist will act as a technology leader, customer advisor, mentor, and solution architect, helping both internal teams and customers adopt AI and Agentic AI solutions that deliver measurable business value.

Key Responsibilities

• Drive widespread adoption of AI tools and practices across engineering teams. Enable engineers to maximize productivity using tools such as GitHub Copilot, Microsoft Copilot, ChatGPT, Claude, Gemini, and emerging AI platforms.
• Act as the AI subject matter expert in customer meetings, workshops, and pre-sales discussions.
• Design and review enterprise-scale AI and Agentic AI solution architectures. Architect solutions involving AI agents, multi-agent systems, RAG, copilots, workflow automation, and intelligent decision-making systems.
• Establish AI engineering standards, reusable frameworks and solutions.

Work Experience

Required Skills & Experience

• Strong experience in software architecture and enterprise solution design.
• Hands-on experience with Generative AI, LLMs, AI Agents, Agentic AI frameworks, and RAG architecture.
• Good understanding of Machine Learning, Deep Learning, NLP, and AI application development.
• Experience with more than one enterprise AI tech stack such as Azure OpenAI, OpenAI, Anthropic, Gemini, AWS Bedrock, or similar.
• Proficiency in Python and familiarity with AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Proven experience engaging with customers, presenting technical solutions with confidence, and providing technology leadership.
• Knowledge of Responsible AI, AI Governance, and AI Security practices.

Soft Skills

• Proven track record in mentoring, coaching, and having a knowledge-sharing mindset.
• Excellent communication and presentation skills with both technical and business stakeholders.
• Ability to influence teams, drive change, and build confidence with customers.

Nice to Have

• Similar experience in establishing AI Centers of Excellence (CoE) or leading technology transformation initiatives.
• Experience with LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen, or similar AI frameworks.
• Knowledge of MLOps, LLMOps, APIs, microservices, and modern software engineering practices.