(ML OPNS Lead)Machine Learning Operations Lead Engineer (Mangalore)

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 high-performing cloud-based infrastructures across Azure and AWS. - Lead end-to-end 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 infrastructure-as-code (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 . - Hands-on experience with Python ML libraries (TensorFlow, PyTorch, Scikit-learn). 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. - Hands-on 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 end-to-end 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 (Good 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. .