[Skip to main content](#main-content) [OpenNash Jobs](https://jobs.opennash.com/) [Hiring report](/reports/hiring/) · [All Tata Consultancy Services jobs](https://jobs.opennash.com/companies/tata-consultancy-services/) · [Search jobs](/search/) Tata Consultancy Services · Chennai # MLOps Engineer Chennai Operations Last seen on Tata Consultancy Services’s official careers site on August 1, 2026; it may have closed since. [Apply on Tata Consultancy Services’s careers site →](https://ibegin.tcsapps.com/candidate/next/en-IN/jobs/424768J) [OpenNash Jobs](https://jobs.opennash.com/) / [Tata Consultancy Services](https://jobs.opennash.com/companies/tata-consultancy-services/) / MLOps Engineer ## Job description ### Job Title MLOps Engineer Experience: 4-15 years (2+ years in AI/Gen AI is must). ### Job Location PAN India (metro cities) ### Required Skills - Degree in computer science, artificial intelligence, or IT. - 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).