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MLOps GenAI Engineer

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Onsite

MLOps GenAI Engineer

Tata Consultancy Services

MLOps GenAI Engineer

Skills

Google Cloud PlatformSQL QueriesData EngineeringDockerPythonPyTorchTensorFlowMachine LearningAIGitHubKubernetesTerraform

1 month ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

18 September 2026

What they need


TCS is hiring

Job Title: MLOps / GenAI Engineer (GCP)

Job Summary:

Seeking an experienced Senior MLOps / GenAI Engineer with expertise in building, deploying, and managing Machine Learning and Generative AI solutions on Google Cloud Platform (GCP). The role requires strong experience in Vertex AI, MLOps, model deployment, automation, and cloud-native AI platforms.

Key Responsibilities:

  • Design, develop, and deploy ML and Generative AI solutions using Vertex AI.
  • Build and manage ML pipelines using Vertex Pipelines and Kubeflow.
  • Develop and optimize training workloads, hyperparameter tuning, and model lifecycle management.
  • Implement GenAI solutions using Gemini, Agent Builder, Vector Search, and RAG architectures.
  • Deploy, monitor, and maintain production ML models with drift detection and explainability.
  • Collaborate with data engineering teams using BigQuery, Dataflow, Dataproc, and Spark.
  • Automate ML workflows using CI/CD, Terraform, Docker, Kubernetes, and GKE.

Required Skills:

  • Vertex AI: Workbench, Model Registry, Feature Store, Experiments.
  • MLOps: Vertex Pipelines, Kubeflow, Model Versioning, Metadata, Lineage.
  • GenAI: Gemini, Agent Builder, Vector Search, RAG.
  • Data Processing: BigQuery ML, Dataflow, Dataproc, Spark.
  • Containers & DevOps: Docker, Kubernetes, GKE, Cloud Build, GitHub Actions, Terraform.
  • Languages: Python, SQL, Bash.
  • ML Frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost.

Experience:

  • 5+ years in Machine Learning, MLOps, or Data Engineering.
  • Hands-on experience with Vertex AI and ML model deployment on GCP.
  • Experience building scalable ML/GenAI solutions in cloud environments.
  • Strong background in automation, CI/CD, and containerized deployments

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