MLOps / GenAI Engineer (GCP)

Tata Consultancy Services; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Tata Consultancy Services in Bengaluru seeks an experienced Senior MLOps / GenAI Engineer to design, deploy, and govern ML and Generative AI solutions on Google Cloud Platform. The role emphasizes Vertex AI, MLOps, model deployment, automation, and cloud-native AI platforms.

Key responsibilities include building pipelines with Vertex Pipelines and Kubeflow, tuning training workloads, and applying GenAI techniques like Vector Search and RAG, while ensuring production models are monitored with

Qualifications

• Experience building and deploying ML/GenAI on GCP with Vertex AI.
• Proficient in ML pipelines, model deployment, and lifecycle management.
• Hands-on with Kubeflow, Vertex Pipelines, and cloud-native AI platforms.
• Strong automation skills using CI/CD, Terraform, Docker and Kubernetes.

Responsibilities

• Design, develop, and deploy ML and GenAI solutions on Vertex AI.
• Build and manage ML pipelines with Vertex Pipelines and Kubeflow.
• Optimize training workloads and manage model lifecycle and tuning.
• Implement GenAI using Gemini, Agent Builder, Vector Search, and RAG.
• Deploy, monitor, and maintain production ML models with drift detection and explainability.
• Collaborate with data engineering using BigQuery, Dataflow, Dataproc, and Spark.

Skills

Vertex AI

MLOps

GenAI

GCP

Kubeflow

CI/CD

Terraform

Docker

Kubernetes

GKE

Vector Search

RAG

BigQuery

Dataflow

Dataproc

Spark

Model lifecycle

Hyperparameter tuning

Explainability

Drift detection

Tools

Kubeflow

Docker

Kubernetes

GKE

Terraform

Job description

Role & responsibilities

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