Walk-in | mlops Developer

Tata Consultancy Services; Bengaluru; On-site; INR 1,500,000 - 4,500,000; Full time

Job summary

Tata Consultancy Services is hosting a walk-in drive for an MLOps Developer at the Bengaluru location on 19 Sep 2026. The role requires 4+ years of experience with strong knowledge of MLOps, end-to-end ML lifecycle, and CI/CD for ML workflows.

You will work closely with data scientists, engineers, and platform teams to productionize models using Docker, Kubernetes, and MLflow/Kubeflow. The position focuses on deploying ML workloads on cloud platforms (AWS/Azure/GCP) and implementing robust

Qualifications

• Strong knowledge of MLOps principles and the end-to-end ML lifecycle.

Responsibilities

• Design and implement CI/CD pipelines for ML workflows, including testing, promotion, rollback, and reproducible builds.
• Hands-on with containerization and orchestration (Docker/Kubernetes) and ML pipeline tooling (MLflow/Kubeflow).
• Monitoring and observability for ML systems including drift checks and alerts.
• Deploy and run ML workloads on cloud platforms (AWS/Azure/GCP) using managed services.
• Ensure security, governance, and access controls for production ML.
• Collaborate with data scientists and platform teams to productionize models following best practices.

Skills

MLOps principles

CI/CD pipelines

Docker

Kubernetes

MLflow/Kubeflow

Cloud platforms

Monitoring/observability

Security/governance

collaboration with data scientists

Tools

Docker

Kubernetes

MLflow

Kubeflow

Job description

Tcs Hiring!!

Tcs is having walkin drive for skill Mlops developer at Tcs Banglore location on 19 sep 2026

Exp req:4+

ob description

• Strong knowledge of MLOps principles and the end-to-end ML lifecycle: data preparation training validation deployment/serving monitoring/refresh pipelines.
• Design and implement CI/CD (and CT/continuous training) pipelines for ML workflows, including testing, promotion, rollback, and reproducible builds.
• Hands-on with containerization and orchestration (e.g., Docker/Kubernetes) and ML pipeline tooling such as MLflow/Kubeflow (or equivalent).
• Monitoring & observability for ML systems: service + data + model health tracking, drift checks (feature/target/concept), alerts/triggers, and root-cause analysis.
• Cloud platform experience (AWS/Azure/GCP) to deploy and run ML workloads using managed services and cloud-native components (e.g., GKE, BigQuery, Cloud Storage, Vertex AI capabilities).
• Security, governance, and access controls: authentication/authorization, encryption, policy/guardrails, and compliance-focused logging/traceability for production ML.
• TJob descriptionl collaboration with data scientists, engineers, and platform teams to productionize models following best practices for repeatability, standardization, and operational efficiency.T