Description

You will design, build, and secure AI/ML platforms on AWS and Azure, driving adoption through reusable patterns and automated MLOps pipelines.

This role is hybrid.

Responsibilities

• Evaluate and enable AWS and Azure AI/ML services (SageMaker, Bedrock, Azure OpenAI, Azure AI Foundry) via proof-of-concepts.
• Design and implement secure architectural patterns including private endpoints, customer-managed keys, and service-to-service authentication.
• Build end-to-end MLOps platforms and automated pipelines for model training, evaluation, deployment, and monitoring.
• Develop infrastructure-as-code and automation frameworks to accelerate AI/ML adoption and integration.
• Implement observability solutions with drift detection and produce technical reports on security, compliance, and cost analysis.

Required Skills

• 5–7 years of cloud engineering experience, with 3+ years focused on AI/ML platforms.
• Deep hands-on expertise with AWS AI/ML services: SageMaker (training, pipelines, inference, JumpStart) and Bedrock.
• Deep hands-on expertise with Azure AI/ML services: Azure Machine Learning, Azure OpenAI, and Azure AI Foundry.
• Strong knowledge of LLMOps, LLM lifecycle management, agentic AI, RAG, and prompt engineering.
• Proficiency in Python and infrastructure-as-code tools: Terraform, CloudFormation, ARM/Bicep.
• Experience implementing guardrails, governance, and security patterns (private endpoints, network isolation) for AI/ML services.
• Experience with MLflow or similar tools for experiment tracking and model registries.
• Strong understanding of cloud networking in regulated environments and Agile delivery.

Preferred Skills

• AWS or Azure AI/ML certifications.
• Experience with vector databases, embedding models, and model optimization.

Key Skills

Education

Any Gradute