Cloud AI Platform Architect

Key Responsibilities:

• AI Platform & Infrastructure Engineering and Architecture Leadership

• 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.

• Client & Stakeholder Management

• 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

• Cloud, Automation & Deployment Engineering Leadership

• 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.

• MLOps, Data Pipelines & Operationalization Leadership

• 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.

• Asset and Accelerator Development

• Lead creation of EY IP including AI platform reference architectures, reusable IaC m…