Cloud AI Engineer

EY; Chennai District, Bengaluru; On-site; INR 3,000,000 - 5,500,000; Full time

Job summary

EY is seeking a senior Cloud & AI Platform Engineer to drive enterprise AI platform implementations across BFSI, manufacturing, healthcare, and public sector. You will design scalable architectures, host AI models, and oversee secure runtimes in multi-cloud environments.

The role emphasizes hands-on engineering, DevOps, MLOps, and governance, with focus on sovereign AI, regulatory deployments, and cross-functional leadership to drive platform transformation.

Qualifications

• B.Tech/B.E. in CS/IT/AI/ML mandatory; M.Tech/MS preferred.
• Hands-on experience designing enterprise AI platforms, model hosting, inference systems, vector DB infra, and secure runtimes.
• Deep expertise in AWS, Azure and GCP with AI, infra services and multi-cloud focus.
• Terraform, Bicep, ARM, CloudFormation, CI/CD pipelines, Kubernetes, microservices, release engineering.
• Experience with MLOps tools: MLflow, Kubeflow, Vertex AI pipelines, model registry and monitoring.
• Data engineering: ETL/ELT, Airflow, Spark, Databricks, feature stores, streaming/batch pipelines.
• Programming: Python, YAML, JSON, REST APIs, FastAPI; automation scripting.
• Security & governance: governance, secrets, policy, observability, Responsible AI.

Responsibilities

• Lead enterprise AI platform implementations across BFSI, manufacturing, healthcare or public sector.
• Collaborate with cross-functional teams to architect scalable multi-cloud AI infra and governance roadmaps.

Skills

AI Platform Engineering

Cloud Platforms

Automation & Deployment

MLOps / LLMOps

Data Engineering

Programming & APIs

Security & Governance

Leadership

Soft Skills

Education

B.Tech/B.E. (CS/IT/AI/ML mandatory)

M.Tech / MS in Cloud Computing/AI/Data Eng/ML (preferred)

Tools

Terraform

Kubernetes

CI/CD

CloudFormation

Ansible

Airflow

Kubeflow

MLflow

Databricks

Vertex AI

SageMaker

Azure ML

Job description

Required Skills & Qualifications

Education

B.Tech/B.E. (Computer Science / IT / AI / ML mandatory); M.Tech / MS in Cloud Computing, AI, Data Engineering or Machine Learning (preferred).

Core Technical Expertise (Hands-on Implementation Required)

• 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.

AI and Data Science Certifications (Good to have)

• 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

Consulting & Leadership Experience

• 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.
• Experience with sovereign AI, regulated industry deployments and multi-cloud architecture programs.

Soft Skills

• 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.