AI Infrastructure Engineer

Role Overview

We are looking for an experienced MLOps Lead with deep expertise in Azure and AWS cloud ecosystems , who can design, deploy, and manage scalable AI / ML infrastructure. The ideal candidate should bring a strong background in cloud governance, GenAI tooling, automation, and CI / CD pipelines , with hands-on experience across modern MLOps frameworks.

Key Responsibilities

• Design, implement, and manage scalable cloud-based AI / ML infrastructure across Azure and AWS .
• Drive end-to-end MLOps lifecycle — model deployment, monitoring, retraining, and governance.
• Enable GenAI and Agentic AI platforms leveraging Azure OpenAI, Bedrock, Anthropic Claude, LangChain, etc.
• Implement CI / CD pipelines using Azure DevOps or AWS CodePipeline.
• Ensure security, observability, and compliance across ML and GenAI ecosystems.
• Manage infrastructure automation via Terraform, Bicep, CloudFormation , or similar IaC tools.
• Collaborate with data science and engineering teams to optimize ML workflows, data pipelines, and API integrations.
• Implement monitoring and alerting using Grafana, Prometheus, Azure Monitor, and Application Insights.
• Oversee networking, identity management, and role-based access controls (IAM, RBAC) across clouds.
• Support model lifecycle management — drift monitoring, retraining, technical evaluation, and business validation.

Technical Skills & Expertise

Cloud & MLOps Platforms

AI / ML & Programming

DevOps & Infrastructure

Monitoring & Observability

Security & Access Management

Testing & Automation

Good to Have

Preferred Background