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 explicit delivery focus.

📌 Cloud AI Engineer (Chennai)
🏢 EY
📍 Chennai