MLOPS Developer(WALKIN INTERVIEW-19-September)

Tata Consultancy Services; Tamil Nadu; On-site; INR 1,400,000 - 2,400,000; Full time

Job summary

Tata Consultancy Services is seeking an experienced MLOps specialist to design and implement end-to-end ML pipelines on cloud platforms, emphasizing reproducibility, monitoring, and governance.

You will collaborate with data scientists and platform teams to productionize models, ensure robust CI/CD, continuous training, and secure, auditable ML workflows across the lifecycle.

Qualifications

• Experience applying MLOps across data prep, modeling, deployment and monitoring.
• Ability to design end-to-end pipelines with CI/CD and continuous training.
• Proficiency with containerization and orchestration for ML workloads.

Responsibilities

• Design and implement end-to-end ML pipelines with CI/CD and CT.
• Collaborate with data scientists and platform teams to productionize models.
• Manage cloud deployments and ensure security/compliance in ML workflows.
• Build observability for ML systems including drift checks and alerts.

Skills

MLOps principles

ML lifecycle

CI/CD for ML

Continuous Training

Docker/Kubernetes

MLflow/Kubeflow

Monitoring/Observability

Cloud platforms

Security & governance

Cross-functional collaboration

Python / .NET / Java

TensorFlow / PyTorch / Keras

Tools

MLflow

Kubeflow

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).