(ML OPNS Lead)Machine Learning Operations Lead Engineer

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:

• Strong 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.