The AI Lead is responsible for end‑to‑end AI solution architecture and technical leadership across complex client engagements. This role drives scalable, secure, and enterprise‑grade AI implementations, mentors delivery teams, and ensures high‑quality outcomes aligned with GT standards, Responsible AI principles, and business objectives.

Key Responsibilities

Own AI solution architecture, technical design, and implementation strategy

Lead AI delivery across multiple workstreams and teams

Define and enforce standards for AI, GenAI, data, and integration patterns

Design scalable RAG, agent‑based, and orchestration architectures

Guide teams on AI governance, security, privacy, and Responsible AI

Lead client architecture discussions, technical workshops, and executive demos

Review solution designs, code patterns, and delivery artifacts

Mentor and coach consultants, senior consultants, and associates

Core Tools & Technologies

Microsoft Ecosystem

Azure AI Foundry / Azure AI Studio

Azure OpenAI

Advanced prompt engineering

RAG, agent orchestration, evaluation frameworks

Copilot Studio (Premium)

Enterprise copilots, plugins, Direct Line, voice capabilities

Azure AI Search

Azure Integration Services

Azure Functions, Logic Apps

Service Bus, Event Grid

Power Platform





Enterprise‑grade Power Apps, Dataverse, advanced automation

- CI/CD pipelines using Azure DevOps

AWS & Google Cloud

AWS AI / GenAI

Amazon Bedrock (LLM orchestration, multi‑model strategies)

AWS Lambda, event‑driven architectures

S3 and data integration patterns

Google Cloud AI

Vertex AI (GenAI workflows, model integration)

BigQuery (analytics and AI‑driven insights)

GenAI / LLM Platforms

Anthropic Claude

Advanced reasoning, summarization, and enterprise GenAI use cases

Experience designing multi‑LLM and vendor‑agnostic AI architectures

Programming & Engineering

Python (solution frameworks, orchestration, tooling)

REST APIs, integration design

Secure enterprise integration patterns

Skills Required

Strong architecture and solution design skills

Proven technical leadership and mentoring capability

Deep understanding of enterprise AI and GenAI patterns

Stakeholder management, risk identification, and mitigation

- Ability to translate business strategy into scalable AI platforms

Required Certifications

Microsoft Certified: Azure AI Engineer Associate (AI‑102)

Or

AWS Certified Machine Learning – Specialty

Or

Google Skilled Machine Learning Engineer

📌 AI - Lead consultant (Kolkata)
🏢 Grant Thornton INDUS
📍 Kolkata