Walk-in | MLOPS Developer

Tata Consultancy Services; Chennai District; On-site; INR 1,500,000 - 2,600,000; Full time

Job summary

Tata Consultancy Services in Chennai seeks an experienced ML Ops Engineer to own the end-to-end ML lifecycle, build CI/CD for ML models, and productionize pipelines with containerization and orchestration tools. You will collaborate with data scientists, engineers, and platform teams to ensure repeatable, scalable ML deployments.

The role requires hands-on experience with Python and ML frameworks, plus cloud services (AWS/Azure/GCP) and model monitoring practices to ensure reliability and

Qualifications

• Strong knowledge of MLOps principles and the end-to-end ML lifecycle.
• Design and implement CI/CD pipelines for ML workflows with testing and deployment.
• Hands-on with containerization and orchestration (Docker/Kubernetes) and ML tooling.

Skills

MLOps

CI/CD pipelines

Docker/Kubernetes

MLflow/Kubeflow

Monitoring

Cloud platforms

Security/Governance

Collaboration

Python

Java

TensorFlow

PyTorch

Keras

Tools

Docker

Kubernetes

MLflow

Kubeflow

TensorFlow

PyTorch

Keras

GKE

BigQuery

Cloud Storage

Vertex AI

Job description

Job Description(WALK-IN INTERVIEW)

Exp: 4 to 15years

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).