(ML OPNS Lead)Machine Learning Operations Lead Engineer

Role : ML OPS Lead Engineer_ (Machine Learning Operations Lead Engineer)

Job Mode : Remote

Experience : 7+ Years

Notice Period : Immediate / 10 to 15 Days

Role Overview

We are seeking a highly skilled ML Ops Lead Engineer with extensive experience in Machine Learning Operations, Cloud Infrastructure, and Generative AI platforms . The ideal candidate will have deep expertise in both Azure and AWS ecosystems , with a proven track record of designing, deploying, and maintaining scalable and secure ML solutions across hybrid environments.

Key Responsibilities

• Design, deploy, and manage scalable, secure, and reliable cloud-based ML infrastructures leveraging Azure and AWS services.
• Lead ML Ops initiatives to streamline model development, deployment, and monitoring pipelines .
• Collaborate with data scientists, ML engineers, and platform teams to operationalize ML models efficiently.
• Implement and maintain CI / CD pipelines for ML workflows using Azure DevOps or AWS CodePipeline .
• Drive governance, observability, compliance, and audit controls within ML and GenAI environments.
• Refine and enforce security best practices , including IAM, RBAC, Azure Policy, and AWS SCPs.
• Oversee AI evaluation, prompt security scans , and red teaming using Azure AI Evaluation SDK.
• Manage data storage, compute, and networking integrations across S3, DynamoDB, Cosmos DB, RDS, and Blob Storage .
• Build Infrastructure as Code (IaC) using Terraform, ARM / Bicep, CloudFormation , or equivalent tools.
• Implement monitoring and observability solutions using Grafana, Prometheus, Application Insights, and Azure Monitor.
• Support ML model lifecycle management deployment, monitoring, retraining, and drift detection.
• Collaborate with stakeholders to resolve ML pipeline issues and support model infrastructure needs.

Required Skills & Experience

ML Engineering & Model Lifecycle Expertise

Security & Governance

Cloud-Specific Expertise

AWS

Azure