Vidushi Infotech
Databricks & MLOps Engineer
Skills
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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