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.