onsite
Machine Learning Engineer - Tata Consultancy Services (TCS)
ML Engineer
Senior ML Engineer focused on end‑to‑end model lifecycle: development, deployment, and monitoring using MLOps tools (MLflow, Kubeflow, SageMaker) on cloud platforms (AWS, Azure, GCP).
About the role
Key Responsibilities
- Design, build, and maintain scalable data and ML pipelines for production workloads.
- Implement CI/CD workflows for model training, versioning, and deployment using Docker, Kubernetes, and MLOps platforms.
- Integrate and monitor models in cloud environments (AWS SageMaker, Azure ML, GCP Vertex AI) ensuring reliability and performance.
- Collaborate with data scientists to translate research prototypes into production‑ready solutions.
- Develop automated retraining and monitoring pipelines to detect model drift and trigger updates.
Requirements
- Proven experience in Python (NumPy, pandas, scikit‑learn, PyTorch/TensorFlow) and ML model development.
- Hands‑on expertise with MLOps tools such as MLflow, Kubeflow, and cloud‑native services.
- Strong knowledge of containerization (Docker) and orchestration (Kubernetes).
- Experience deploying models on AWS, Azure, or GCP with versioning and monitoring.
- Excellent problem‑solving skills and a track record of delivering production‑ready ML solutions.
Skills
mlopspythondockerkubernetesawstensorflow