Walk-in | Machine Learning Developer

Tata Consultancy Services; Chennai District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Tata Consultancy Services in Chennai is seeking an ML Ops Developer to drive end-to-end ML lifecycle, from data prep to deployment and monitoring. You will design CI/CD pipelines, containerize with Docker/Kubernetes, and work with ML tooling like MLflow or Kubeflow to productionize models.

The role requires 4–15 years of experience, with cloud platforms (AWS/Azure/GCP) and strong collaboration across data science, engineering, and platform teams.

Qualifications

• Strong knowledge of MLOps principles and the end-to-end ML lifecycle: data prep training validation deployment/serving monitoring/refresh pipelines.
• 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 tooling (MLflow/Kubeflow).
• Monitoring and observability for ML systems including drift checks and alerts.
• Cloud platforms (AWS/Azure/GCP) to deploy ML workloads using managed services.
• Security, governance, and access controls for production ML with compliant logging.

Responsibilities

• Design and deliver end-to-end ML pipelines including testing, deployment, and monitoring.
• Collaborate with data scientists, engineers, and platform teams to productionize models.
• Maintain security, governance, and auditability for ML workloads.
• Implement scalable CI/CD/CT pipelines for ML workflows and model lifecycle management.

Skills

MLOps

ML lifecycle

CI/CD for ML

Python/Java/.Net

Cross-team collaboration

Monitoring observability

Tools

Docker

Kubernetes

MLflow

Kubeflow

TensorFlow

PyTorch

Job description

Greetings from TCS!!

Join Walk-in Drive on 19 Sep

Interview Location : Chennai

Role - ML Ops Developer

Experience - 4 to 15 years

Location: Chennai

Job 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.
• Cross-functional collaboration with data scientists, engineers, and platform teams to productionize models following best practices for repeatability, standardization, and operational efficiency.

Proficiency in programming languages such as Python, .Net or Java, with experience in relevant libraries and frameworks (e.g., TensorFlow, PyTorch, Keras).