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Databricks & MLOps Engineer

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Remote

Databricks & MLOps Engineer

Vidushi Infotech

Databricks & MLOps Engineer

Skills

DatabricksMachine Learning

15 days ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Remote

Published on:

8 October 2026

What they need


Databricks & MLOps Engineer

  • Experience: 5 to 8 Years
  • MLOps Experience: Minimum 2 Years
  • Location: Remote India

Role Overview

We are looking for an experienced Databricks & MLOps Engineer to serve as the Databricks Technical Lead for a client program.

The candidate will work closely with the migration and delivery teams, providing technical guidance, governance, implementation support, and hands-on expertise to ensure successful adoption and long-term sustainability of the Databricks platform.

The role requires strong experience in MLOps, ML Engineering, Databricks, ML model deployment, and automated ML pipelines.

Key Responsibilities

  • Act as the technical lead for Databricks within the client program.
  • Partner closely with the migration team to provide technical guidance, governance, and implementation support.
  • Ensure successful onboarding and migration of existing assets to the Databricks platform.
  • Design and develop Databricks-native data and ML pipelines.
  • Build and maintain automated ML pipelines with scheduled triggers for model refresh and retraining.
  • Establish and implement MLOps best practices across the ML lifecycle.
  • Deploy and manage ML models in production using appropriate MLOps frameworks and tools.
  • Assess business and technical requirements and translate them into scalable ML engineering solutions.
  • Ensure ML models are production-ready, maintainable, and scalable.
  • Work as a bridge between migration and delivery teams to ensure consistent architecture and effective platform utilization.
  • Provide coaching, technical guidance, and hands-on support to the existing engineering team.
  • Contribute to the governance, architecture, performance, and long-term sustainability of the Databricks ecosystem.
  • Troubleshoot and resolve issues related to ML pipelines, model deployment, and Databricks workloads.

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