AI Transformation Architect

Job description

AI Transformation Architect

Role Overview

The AI Transformation Architect is responsible for designing, building, and scaling AI-powered business solutions that drive measurable business outcomes. This role combines hands-on technical expertise with strategic leadership, enabling the successful adoption of Artificial Intelligence, Generative AI, Agentic AI, and Intelligent Automation across the enterprise.

The ideal candidate brings experience in architecture, software engineering, data platforms, cloud technologies, or digital transformation, with a proven track record of delivering enterprise-scale solutions and leading multi-disciplinary teams. They are equally comfortable discussing AI strategy with executives as they are reviewing solution architectures, building prototypes, and guiding engineering teams through implementation.

Key Responsibilities

AI Strategy & Transformation

• Partner with business and technology stakeholders to identify, prioritize, and deliver high-value AI use cases.
• Translate business challenges into scalable AI solutions and transformation roadmaps.
• Conduct AI maturity assessments and define adoption strategies aligned to business goals.
• Develop business cases, success metrics, and value realization frameworks for AI investments.
• Advise leadership on emerging AI capabilities, opportunities, risks, and implementation approaches.

Solution Architecture & Hands-On Technical Leadership

• Design end-to-end AI architectures leveraging cloud, data, analytics, Machine Learning, and Generative AI technologies.
• Develop proof-of-concepts, prototypes, and reference implementations to validate solution approaches.
• Architect solutions using:
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• AI Agents and Multi-Agent Systems
• Intelligent Automation
• Machine Learning and Predictive Analytics
• Conversational AI and Assistants
• Define architecture standards, reusable patterns, and best practices for AI solution delivery.
• Collaborate closely with engineering teams on deployment, integration, scalability, security, and performance optimization.
• Remain actively involved in technical design reviews and critical implementation decisions.

Leadership & Delivery

• Lead and mentor cross-functional teams of architects, engineers, data scientists, analysts, and product owners.
• Provide technical leadership across multiple concurrent AI transformation initiatives.
• Drive architecture governance and ensure adherence to enterprise standards.
• Guide delivery teams through solution design, implementation, testing, and production deployment.
• Foster a culture of innovation, learning, and engineering excellence.

AI Governance & Responsible AI

• Implement Responsible AI principles and governance frameworks.
• Ensure compliance with security, privacy, risk, and regulatory requirements.
• Define monitoring, evaluation, and operational processes for AI systems.
• Establish best practices for model lifecycle management and AI operationalization (MLOps/LLMOps).

Stakeholder Management

• Engage effectively with business leaders, executives, and technology teams.
• Facilitate discovery workshops, architecture reviews, and executive briefings.
• Communicate complex technical concepts to both technical and non-technical audiences.
• Build trusted relationships across business functions and technology organizations.

Technical Skills Required

AI & Data

• Hands-on experience on frontier models
• Generative AI, LLMs, RAG, AI Agents
• Machine Learning and Predictive Analytics
• MLOps and LLMOps
• Data Architecture and Integration
• Knowledge Management and AI Search

Cloud & Platforms

• Microsoft Azure (preferred), AWS, or Google Cloud
• Azure OpenAI Service
• AI Foundry / AI Studio
• Microsoft Fabric
• Databricks or equivalent analytics platforms
• API-driven architectures and microservices

Development

• Python
• REST APIs
• Git-based CI/CD solutions
• Containerization and cloud-native architectures
• Ability to build prototypes and reference implementations