Senior MLOps Engineer

We are looking for a skilled and experienced MLOps Engineer to join our team and play a key role in deploying, maintaining, and monitoring machine learning models in production environments. This role requires a solid understanding of cloud-based infrastructure, Databricks, automation, and MLOps best practices. You will collaborate closely with data scientists, engineers, and DevOps teams to ensure scalable, secure, and efficient machine learning operations.

Key Responsibilities:

1. Model Deployment:

Deploy machine learning models into production environments on Azure, AWS, and Databricks.

Collaborate with data scientists and engineering teams to integrate ML models into existing business systems and pipelines.

2. Infrastructure Management:

Set up and manage infrastructure for scalable ML model training and deployment using cloud platforms and Databricks.

Implement CI/CD pipelines for ML workflows using tools like Azure DevOps, GitHub Actions, and Databricks Workflows.

3. Monitoring and Maintenance:

Monitor model performance, data drift, concept drift, and system health using appropriate observability tools.

Implement alerting systems and dashboards for real-time monitoring and quick issue resolution. 4. Cloud & Platform Expertise:

Hands-on experience with Azure Machine Learning Services, AWS Sagemaker, and Databricks.

Use Databricks MLflow, Delta Lake, and other tools for model tracking, experiment management, and data versioning.

5. Automation and Scripting:

Automate model deployment and workflow orchestration using Python, Databricks Jobs, and Airflow.

Implement Infrastructure as Code (IaC) using Terraform, ARM templates, or CloudFormation for scalable deployments.

Required Skills & Qualifications:

6+ years exp. in MLOps, DevOps, or related fields with direct experience in ML model deployment and monitoring.

Cloud Platforms: Proficient in Databricks Azure, and AWS environments.

MLOps Tools: Hands-on with Kubernetes, Docker, Apache Airflow, MLflow, Databricks, and Delta Lake.

CI/CD: Experience building robust CI/CD pipelines for ML using Git, Azure DevOps, Jenkins, or GitHub Actions.

Automation & Scripting: Strong programming/scripting in Python. Familiarity with shell scripting is a plus.

IaC: Experience in provisioning infrastructure using Terraform or similar tools.

Model Governance: Understanding of model versioning, governance, reproducibility, and compliance in enterprise environments.

Problem Solving: Ability to identify bottlenecks, optimize workflows, and deliver scalable solutions across the ML lifecycle.