Role ML OPS Lead Engineer Job Mode Remote Experience 7 Years Notice Period Immediate 10 to 15 Days Experience Required 7 years in platform or infrastructure engineering with significant experience in ML Ops AI and Cloud Azure AWS Key Responsibilities Design deploy and manage scalable secure and highperforming cloudbased infrastructures across Azure and AWS Lead endtoend ML Ops lifecycle including model deployment monitoring retraining and CI CD integration Collaborate with AI ML Data Science and DevOps teams to automate model lifecycle management and streamline ML workflows Architect and implement governance compliance observability and security frameworks for ML and GenAI systems Drive innovation in Generative AI and Agentic AI ecosystems integrating services like Azure OpenAI Bedrock Anthropic Claude and OpenAI API Implement infrastructureascode IaC practices using Terraform Bicep ARM or CloudFormation Manage networking IAM and security configurations across Azure and AWS environments Establish monitoring alerting and performance dashboards using Grafana Prometheus Azure Monitor and Log Analytics Required Technical Skills Cloud Platforms Azure Azure AI Services Azure Search Azure ML Databricks AKS Azure AI Foundry Azure AI Hub AWS SageMaker Bedrock Lambda ECS CDK CloudFormation AI ML Generative AI Exposure to Generative and Agentic AI ecosystems Azure OpenAI Bedrock Claude LlamaCloud LangChain Understanding of token usage prompt injection jailbreak risks and mitigation methods Experience with Azure AI Evaluation SDK and AI Red Teaming Prompt Security Scans Handson experience with Python ML libraries TensorFlow PyTorch Scikitlearn DevOps Automation Robust experience with Azure DevOps AWS CodePipeline for CI CD setup and management Familiarity with Docker Kubernetes and container orchestration Knowledge of IaC tools Terraform ARM Bicep CloudFormation Database Storage Azure Blob Storage Cosmos DB SQL Key Vault Data Lake Storage AWS S3 DynamoDB RDS Redshift Aurora Understanding of OLTP and OLAP systems Networking Security Proficiency in DNS VPNs Load Balancing VNets IAM and access control RBAC SCP Azure Policy Familiarity with Microsoft AD and principles of least privilege Handson with KMS Key Vault and identity governance best practices ML Engineering Workflow Management Experience using Azure Machine Learning Studio SDK v2 CLI v2 for model monitoring retraining and deployment Build and optimize endtoend ML workflows for production environments Implement drift monitoring model retraining and technical business validation processes Collaborate with data scientists for model deployment and performance optimization Additional Skills Positive to Have Experience with code assistant tools GitHub Copilot Cursor Claude Code Familiarity with Azure Bot Framework APIM Application Gateway Exposure to M365 Copilot and related ecosystem tools Proficiency with AWS Python SDK Boto3 and AWS CDK Testing Quality Implement unit and integration testing in CI CD workflows preferably using ADO Ensure testing and validation coverage for ML pipelines and infrastructure deployments Preferred Qualifications Bachelor s or Master s in Computer Science Information Technology or related field Certification s in Azure AI Engineer AWS Machine Learning Specialty or DevOps highly desirable

📌 ML OPNS LeadMachine Learning Operations Lead Engineer Mangalore
🏢 Epergne Solutions
📍 Mangalore