MLOps Engineer

Job Title: MLOps Engineer

Job Purpose: The MLOps Engineer is responsible for designing, implementing, and maintaining scalable machine learning pipelines and environments. The role ensures seamless integration between data science, engineering, and IT operations, optimizing workflows and automating model deployment and monitoring.

Key Responsibilities:

• Environment Management:

Design, configure, and maintain development, staging, and production environments for machine learning workflows.

• Machine Learning Platforms:

Work with platforms such as DataRobot and Databricks to automate model training, deployment, and monitoring.

• Data Warehousing:

Integrate and manage data pipelines with DWH solutions including Netezza and DataStage for efficient data extraction, transformation, and loading (ETL).

• API Development & Integration:

Develop, deploy, and maintain APIs for model serving and integration with internal/external systems.

• Containerization:

Utilize Docker for packaging and deploying machine learning models and related services, ensuring portability and scalability.

• Automation & CI/CD:

Implement continuous integration and continuous deployment (CI/CD) pipelines for ML workflows.

• Monitoring & Maintenance:

Ability to create dashboards & monitor deployed models for performance, drift, and reliability. Implement automated retraining and alerting mechanisms.

• Collaboration:

Work closely with data scientists, engineers, and IT teams to streamline processes and ensure best practices in model lifecycle management.

Required Skills and Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• Experience with MLOps tools and platforms (DataRobot, Databricks).
• Strong proficiency in data warehousing technologies (Netezza, Datastage).
• Hands-on experience with API development (RESTful APIs).
• Proficiency in Docker and container orchestration.
• Familiarity with cloud environments and deployment strategies.
• Knowledge of CI/CD tools and practices.
• Excellent problem-solving and communication skills.Preferred Skills:
• Experience with orchestration tools (e.g., Kubernetes).
• Familiarity with monitoring tools and frameworks.
• Understanding of data governance and security best practices.