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: Robust 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 / Profes
📌 Cloud AI Platform Engineer (Delhi)
🏢 EY
📍 Delhi