Role overview

Mandatory/core: Strong Databricks + MLOps, specifically ML/AI use cases rather than ETL-only Databricks. Candidates should have real production-grade experience, particularly deploying ML models/AI agents, CI/CD, infrastructure, evaluation, and monitoring. AWS is positive-to-have, since Databricks will be hosted on AWS. The requirement is primarily around production AI engineering on Databricks, rather than conventional Databricks data engineering.

The engineers are expected to take agents/models developed by Data Science teams into production, covering deployment pipelines, testing, serving, observability, monitoring, and operational controls. AWS capability is secondary to the Databricks/AI requirement. Profiles having only traditional data engineering or POC-level GenAI experience would not meet the core requirement.

Support an enterprise AI program for a major financial institution. We need senior engineers with hands-on Databricks AI agent and MLOps experience who can take agents and trained models into production with limited handholding.

Business data scientists develop and train the agents. This engineering team owns the pipelines, infrastructure integration, testing, deployment, monitoring, and operational controls needed to run those agents reliably in production.

Core requirements

Demonstrated experience productionizing AI agents on Databricks, with clear ownership of implementation and deployment.

Hands-on Databricks Genie experience relevant to the client’s implementation. The client referred to the capability as “Genie One”; the exact product/version is being confirmed.

Strong Databricks MLOps experience deploying and operating trained models and AI workloads.

Experience building deployment pipelines and promoting agents/models through development, test, UAT, and production.

Experience implementing observability,



telemetry, monitoring, and troubleshooting for production AI.

Experience building controls to detect and contain unexpected agent behavior.

Senior enterprise engineering experience and the ability to contribute independently.

Traditional Databricks ETL/data engineering experience alone does not meet this requirement. This role focuses on production AI engineering rather than model research or training.

Responsibilities

Take agents and trained models handed over by data scientists and prepare them for production.

Build and maintain deployment pipelines, automation, and environment configurations.

Implement supporting infrastructure and coordinate required data access and integration.

Perform unit testing, support UAT, and validate production readiness.

Instrument AI workloads to capture operational telemetry and monitor agent behavior.

Implement safeguards and intervention mechanisms for unexpected agent behavior.

Troubleshoot production issues and support reliable ongoing operation.

Participate in knowledge transfer and collaborate with engineering and business teams across India and the United States.

Preferred secondary skills

AWS SageMaker.

AWS ECS and EKS.

AWS MLOps and production model deployment.

Experience supporting sensitive data and controlled enterprise environments.

The anticipated work mix is approximately 70% Databricks and 30% AWS. Databricks AI expertise is the primary requirement for all three positions.

Engagement requirements

Ability to work from the designated delivery center in Bangalore or Hyderabad.

Availability for the January 2027 target start.

Right-to-hire eligibility, with any employment or conversion restrictions disclosed upfront.

Extension or conversion is subject to business priorities and approved funding.

This is an India-based engagement; U.S. relocation or visa sponsorship is not included.

📌 Machine Learning Engineer (Bengaluru)
🏢 TEKsystems
📍 Bengaluru