Mlops Engineer — Databricks

MLOps Engineer — Databricks

Client : A large global enterprise (name not disclosed)

Location : India

Work Model : 100% Remote

Contract : 6 months (initial) with possibility of extension

Start Date : ASAP

Engagement : Full-time / Long-term contract

Role Overview

We are seeking an experienced Databricks MLOps Developer to design, build, and manage scalable machine learning operations on the Databricks Lakehouse Platform. The role involves automating ML workflows, operationalizing models, enabling reproducible pipelines, and ensuring governance and monitoring across the ML lifecycle.

Key Responsibilities

1. Develop Scalable MLOps Pipelines

Build automated ML pipelines for training, validation, deployment, and batch / real-time inference.

Use Databricks Workflows, Jobs, Repos , and Delta Live Tables where applicable.

Implement distributed training and inference pipelines using MLflow + PySpark .

2. Model Lifecycle Management

Manage model versioning and promotion across dev → staging → production using MLflow Model Registry .

Create reproducible workflows for model packaging, deployment, and rollback.

3. CI / CD Integration

Build and integrate ML pipelines with CI / CD using Azure DevOps, GitHub Actions, or Jenkins .

Automate testing, validation, and deployment for ML artifacts, notebooks, and infrastructure.

4. Feature Engineering & Data Pipelines

Collaborate with Data Engineering teams to build optimized Delta Lake pipelines (Bronze / Silver / Gold architecture).

Implement feature engineering workflows and support feature reuse at scale.

5. Monitoring & Governance

Set up model monitoring for performance, drift, data quality, and lineage.

Use Databricks-native tools, MLflow metrics, and cloud monitoring services (Azure / AWS).

Ensure compliance through logging, auditing, permissions, and environment governance.

6. Cross-Functional Collaboration

Work closely with Data Scientists, Data Engineers, Cloud teams, and Product teams.

Document workflows, best practices, and MLOps reusable components.

Required Skills & Qualifications

Strong hands-on experience with Databricks (Workflows, Repos, Jobs, Compute)

Proficiency with MLflow (Tracking, Registry, Model Deployment)

Expertise in Delta Lake , PySpark, and distributed data pipelines

Solid programming skills in Python and SQL

Experience with CI / CD tools : Azure DevOps, GitHub Actions, Jenkins

Familiarity with cloud platforms : Azure, AWS, or GCP

Understanding of containerization (Docker) and orchestration (Kubernetes)

Background in ML model training, serving, and observability

Preferred Qualifications

Databricks certifications :

Databricks Certified Machine Learning Professional

Databricks Certified Data Engineer Associate / Professional

Experience with Unity Catalog for governance

Experience implementing feature stores

Knowledge of ML observability tools (WhyLabs, Monte Carlo, Arize AI, etc.)