Overview

The Manager will play a pivotal role in building and scaling EY GDS's AI Strategy & Transformation practice.

Responsibilities

• Lead the design and implementation of enterprise AI platforms that support AI/ML, GenAI, Agentic AI and RAG workloads across hybrid and multi-cloud environments.
• Architect scalable cloud-native AI foundations using AWS SageMaker and Bedrock, Azure ML and Azure OpenAI, GCP Vertex AI and enterprise Kubernetes platforms such as EKS, AKS and GKE.
• Build secure and resilient infrastructure for AI model development, training, deployment and runtime operations including compute, GPU environments, storage, networking, secrets management and access control.
• Design reusable platform services for model hosting, inference endpoints, vector databases, prompt orchestration, agent runtime support and enterprise API integration.
• Establish platform observability with centralized logging, monitoring, tracing, telemetry, performance diagnostics and cost optimization for AI systems.
• Enable secure AI platform controls with policy enforcement, access governance, auditability and support for Responsible AI, compliance and risk requirements.
• Drive standardization of AI platform architecture through reusable patterns, landing zones, environment templates and enterprise engineering best practices.
• Serve as primary technical advisor to CxOs, account leadership teams and enterprise engineering stakeholders on AI platform strategy, cloud modernization and deployment architecture.
• Conduct technical workshops demonstrating platform blueprints, deployment models, MLOps capabilities, observability approaches and AI operational readiness.
• Bridge platform engineering with business strategy by translating complex infrastructure capabilities into scalable business outcomes and delivery roadmaps.
• Build trusted relationships through hands-on PoC delivery, architecture discussions and strategic guidance on AI platform adoption.
• Lead automation of AI infrastructure provisioning using Infrastructure as Code tools such as Terraform, Bicep, ARM templates and CloudFormation.
• Design and implement CI/CD and CT pipelines for AI platforms, model deployment services, APIs, microservices and environment lifecycle management.
• Drive containerization and orchestration patterns using Docker and Kubernetes including workload isolation, autoscaling, high availability and release automation.
• Build deployment frameworks for AI and GenAI applications using REST services, FastAPI, model serving frameworks, serverless patterns and integration middleware.
• Automate configuration management, secrets handling, policy validation, artifact promotion and environment provisioning across dev, test and production landscapes.
• Implement release engineering practices including blue-green deployment, canary rollout, rollback mechanisms and platform upgrade planning.
• Ensure platform reliability, scalability and supportability through automated testing, operational runbooks, resilience engineering and incident readiness.
• Lead the setup and scaling of MLOps and LLMOps capabilities including experiment tracking, model versioning, model registry, artifact management, pipeline orchestration and deployment governance.
• Establish standardized operationalization pipelines for model training, validation, packaging, deployment, monitoring and retraining across enterprise AI use cases.
• Drive integration of AI platforms with batch and real-time data pipelines using Airflow, Prefect, Spark, Kafka, Databricks and cloud-native data services.
• Design and govern reliable data flows for AI workloads including ingestion, transformation, feature engineering, feature store integration, vector store enablement and metadata lineage.
• Implement monitoring for models and services covering drift detection, latency, throughput, failure diagnostics and service-level performance.
• Collaborate closely with data science, AI engineering and application teams to productionize prototypes, notebooks and GenAI use cases into enterprise-grade deployments.
• Drive operational best practices around reproducibility, traceability, auditability and support for regulated or risk-sensitive environments.
• Lead creation of EY IP including AI platform reference architectures, reusable IaC modules, deployment blueprints, MLOps templates and observability accelerators.
• Develop reusable frameworks for secure AI environment setup, platform monitoring dashboards, model deployment factories and RAG infrastructure enablement.
• Package technical accelerators as EY market offerings for rapid client deployment across industries and cloud ecosystems.
• Lead technical solutioning for AI platform transformation RFPs including architecture blueprints, deployment approaches, cloud landing patterns and live PoC demonstrations.
• Develop industry-specific GTM strategies that combine EY assets with hyperscaler AI platform services and enterprise automation capabilities.

Qualifications

• Exceptional technical storytelling for CxO audiences.
• Proven ability to influence senior stakeholders through working prototypes, architecture deep dives and platform transformation roadmaps.
• Leadership of diverse technical teams with clear delivery focus.
• Hands-on experience in building production-grade cloud AI platforms, automated deployment frameworks, MLOps pipelines and enterprise AI infrastructure is mandatory for interview stage.

Education

• AI Platform & Infrastructure Engineering: Strong hands-on experience in designing and managing enterprise AI platforms, model hosting environments, inference systems, vector database infrastructure, API-based AI services and secure runtime environments.
• Cloud Platforms: Deep expertise in AWS, Azure and GCP with focus on AI and infrastructure services such as SageMaker, Bedrock, Azure ML, Azure OpenAI, Vertex AI, AKS, EKS, GKE, IAM, networking, storage and monitoring.
• Automation & Deployment Engineering: Strong knowledge of Terraform, Bicep, ARM, CloudFormation, CI/CD pipelines, containerization, Kubernetes, deployment automation, microservices architecture and release engineering.
• MLOps / LLMOps: Experience with MLflow, Kubeflow, Azure ML pipelines, Vertex AI pipelines, model registry, experiment tracking, model serving, deployment governance and monitoring.
• Data Engineering & Operationalization: Understanding of ETL and ELT pipelines, Airflow, Prefect, Spark, Kafka, Databricks, feature stores, streaming and batch processing and production data pipelines for AI workloads.
• Programming & APIs: Proficiency in Python, shell scripting, YAML, JSON, REST APIs, FastAPI and automation scripting for platform and cloud operations.
• Security & Governance: Familiarity with platform security, secrets management, policy controls, auditability, observability and support for Responsible AI and enterprise governance requirements.
• Microsoft Certified: Azure AI Engineer Associate / Azure DevOps Engineer / Azure Solutions Architect
• AWS Certified Machine Learning – Specialty / AWS DevOps Engineer / AWS Solutions Architect
• Google Professional Machine Learning Engineer / Professional Cloud DevOps Engineer
• Additional: Kubernetes certifications (CKA / CKAD), Terraform Associate, Databricks certifications, MLOps or cloud platform engineering certifications
• 10–13 years in cloud platform engineering, AI platform engineering, DevOps, MLOps or AI consulting (Big 4 / tech preferred).
• Proven track record leading enterprise AI platform implementations across BFSI, manufacturing, healthcare or public sector.