MLOps Engineer

Tata Consultancy Services; Hyderabad; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Tata Consultancy Services Hyderabad is seeking an experienced MLOps Platform Engineer to design and implement enterprise-scale ML platforms on Azure Cloud. You will build deployment architectures using AKS and develop CI/CD and CT pipelines for ML workflows.

You will automate packaging, deployment, retraining, and rollback strategies, integrate Azure ML, AKS, Azure DevOps, GitHub Actions, and container tech, and collaborate with Data Scientists and DevOps teams to ensure secure, scalable, and

Qualifications

• Experience designing enterprise-scale MLOps platforms on Azure.
• Ability to design and optimize AKS-based ML deployment architectures.
• Experience building CI/CD and CT pipelines for ML workflows.
• Knowledge of model packaging, deployment, retraining, and rollback strategies.

Responsibilities

• Design and implement enterprise-scale MLOps platforms on Azure Cloud.
• Build and manage ML model deployment architectures using AKS.
• Develop CI/CD and CT (Continuous Training) pipelines for ML workflows.
• Automate model packaging, deployment, retraining, and rollback strategies.
• Integrate Azure Machine Learning, AKS, Azure DevOps, GitHub Actions, and container technologies.
• Collaborate with Data Scientists, Data Engineers, Solution Architects, and DevOps teams.
• Implement model monitoring, drift detection, performance tracking, and alerting mechanisms.
• Ensure platform security, compliance, and governance standards.
• Optimize AKS clusters for performance, scalability, and cost efficiency.
• Establish MLOps best practices, reusable frameworks, and deployment standards.

Skills

MLOps

Azure Cloud

CI/CD for ML

AKS (Azure Kubernetes Service)

Tools

Azure Kubernetes Service (AKS)

Azure Machine Learning

Azure DevOps

GitHub Actions

Container technologies

Job description

Role & responsibilities

• Design and implement enterprise-scale MLOps platforms on Azure Cloud.
• Build and manage ML model deployment architectures using Azure Kubernetes Service (AKS).
• Develop CI/CD and CT (Continuous Training) pipelines for ML workflows.
• Automate model packaging, deployment, retraining, and rollback strategies.
• Integrate Azure Machine Learning, AKS, Azure DevOps, GitHub Actions, and container technologies.
• Collaborate with Data Scientists, Data Engineers, Solution Architects, and DevOps teams.
• Implement model monitoring, drift detection, performance tracking, and alerting mechanisms.
• Ensure platform security, compliance, and governance standards.
• Optimize AKS clusters for performance, scalability, and cost efficiency.
• Establish MLOps best practices, reusable frameworks, and deployment standards.