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Job Description – Transformation Architect – AI / Automation (Manager)

Function: Clients & Industries, EY Global Delivery Services India LLP

As part of EY’s GMx (Global Managed Services Transformation) initiative, we are re imagining how next-generation managed service transformations are designed and delivered—leveraging modern AI, machine learning, deep learning, automation, cloud-native engineering, and intelligent operations to build future-ready, insight-driven service delivery models.

The Opportunity

As part of EY’s GMx (Global Managed Services Transformation) initiative, this role leads AI driven transformation/ modernization across Managed Services. You will lead, architect, and deliver next generation solutions using modern AI (Gen AI, Agentic AI) , machine learning, deep learning, MLOps, automation, with cloud native engineering to transform managed service operations into scalable, cost effective, value driven, insight led, AI enabled service models. This role enables EY to accelerate value, enhance operational outcomes, and build the future ready Managed Services operations.

Your Key Responsibilities

• Lead and architect AI driven transformation initiatives for Managed Services.
• Hold customer discussions to enable the customer with required information and give direction the program.
• Act as tech lead for the AI led managed service transformation programs - taking value driven tech decisions.
• Lead end to end delivery of AI modernization modules under transformation programs (design → build → deploy → ship). Own up part of program management activities.
• Deliver AI/ML-driven transformation across multi year managed services engagements.
• Lead and mentor cross-functional teams (AI engineers, ML engineers, automation developers, data engineers, cloud specialists).
• Define governance, delivery frameworks, KPIs, quality standards, and operational oversight.
• Translate client needs into AI/automation/data use cases, backlogs, and solution roadmaps.
• Identify modernization opportunities using analytics, automation discovery, and process re-engineering.
• Drive continuous improvement using AIOps, observability, predictive insights, and automation-first processes.
• Ensure security, compliance, data protection, and operational stability across all AI and data platforms.

Technical Expertise Requirements

• Use Cases & Architecture (Must)
• Ability to quickly understand new business processes and identify high ROI AI/ML transformation opportunities.
• Hands-on experience in architecting (technical + solution architecture) AI/ML-driven transformation assets and platforms.
• Expertise in embedding security, responsible/ethical AI, and data governance into AI architectures while ensuring clear RoI and value realization.
• Modern AI & Agentic AI (Must)
• Hands-on experience with LLMs, GenAI, and Agentic AI systems deployed in production on cloud using cloud native capabilities.
• Expertise in multi-agent orchestration, tool-use agents, workflow agents, and enterprise-safe agentic architectures.
• Experience with vector databases, embeddings, RAG pipelines, and AI orchestration frameworks.
• Machine Learning & Deep Learning (Must)
• Strong grounding in supervised/unsupervised ML, tree-based models, forecasting, and statistical modeling.
• Experience with CNNs, RNNs/LSTMs, Transformers, and generative models.
• Proficiency in sklearn, MLlib, PyTorch, TensorFlow, or equivalent frameworks.
• Ability to build, optimize, validate, and deploy ML/DL models at enterprise scale.

MLOps & AI Platform Engineering (Must)

• Strong understanding of CI/CD for ML, model registries, feature stores, drift detection, and model monitoring.
• Hands-on experience with Azure ML, Databricks, AWS Sagemaker, Vertex AI, or equivalent enterprise AI platforms.

Cloud-Native AI Engineering (Must)

• Ability to build, deploy, and operate containerized, microservices based AI workloads on Azure/AWS/GCP using Docker, Kubernetes, AKS/EKS/GKE.
• Experience with serverless AI, event-driven architectures, GPU optimization, and scalable inference patterns.
• Data Engineering (Desirable)
• Familiarity with lakehouses, ETL/ELT, streaming pipelines, and real-time analytics.
• Experience with Databricks, Spark, and cloud-native data engineering services.
• ________________________________________
• To Qualify for the Role, You Must Have
• Ability to lead Transformation programs (multiple modules).
• 8–12 years of experience in AI-driven technology delivery; 3–6+ years in AI/ML leadership roles.
• Deep expertise in modern AI, ML/DL, MLOps, automation, and cloud-native engineering.
• Strong Python development background.
• Experience building, deploying, and shipping large-scale AI products in cloud-native production environments.
• Experience identifying high-value AI use cases through data-driven value quantification.
• Proven track record delivering complex AI-driven transformation programs.
• Strong stakeholder and client management skills.
• Preferred certifications: Cloud (Azure/AWS/GCP), AI/ML, Technical Architecture for AI/ML.

Skills and Attributes for Success

• Strong analytical, conceptual, and problem solving skills.
• Excellent communication, presentation, and stakeholder engagement abilities.
• Quality-oriented and detail-focused mindset.
• Ability to work independently and manage complex multi-disciplinary programs.
• Effective leadership and mentoring skills across global teams.

Core Capabilities

• Methodical and structured thinking.
• Strong analytical and reasoning abilities.
• Effective communication and executive presence.
• Results-driven and quality-focused delivery mindset.

Functional Capabilities

• Ability to operate in dynamic, cross-functional, global environments.
• Strong team collaboration and ability to manage ambiguity.
• Highly motivated, proactive, and outcome-oriented.
• Strong organizational, prioritization, and multitasking abilities.

Additionally, You’ll Also Have

• Creative problem-solving capability.
• Self-driven attitude and strong ownership.
• Logical reasoning and high attention to detail.
• Ability to work across cultures and global teams.
• Strong networking and cross-team collaboration.
• Ability to build trust, strengthen team cohesion, and celebrate success.

What We Look For

• Experience with enterprise AI platforms and cloud-native deployment patterns.
• Strong grounding in MLOps, AIOps, LLMOps, automation, and observability.
• Experience supporting transformation in Managed Services environments.
• Strong communication, planning, and stakeholder alignment skills.

What We Offer

• Continuous learning: Develop future-ready AI, automation, and cloud capabilities.
• Transformative leadership: Coaching and experiences to help you grow as a leader.
• Diverse and inclusive culture: A place where you're empowered to bring your authentic self.

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