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ML Ops Engineer
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Overview:
The ML Ops Engineer designs, implements, and maintains robust machine learning operational pipelines for scalable, reliable, and secure model deployment. This role bridges data science and engineering, ensuring models transition seamlessly from development to production while meeting performance, compliance, and governance standards.
Responsibilities:
Requirements:
Role Overview
The ML Ops Engineer designs, implements, and maintains robust machine learning operational pipelines for scalable, reliable, and secure model deployment. This role bridges data science and engineering, ensuring models transition seamlessly from development to production while meeting performance, compliance, and governance standards.
Key Responsibilities
ML Pipeline Automation- Build and maintain CI/CD pipelines for ML models using Git, Azure DevOps, or similar tools.
- Automate model packaging, artifact storage, versioning, and deployment.
- Platform Integration
- Integrate ML platforms (Snowflake, Azure ML, Git-based pipelines) for end-to-end lifecycle support.
- Collaborate with data engineers to design feature stores and manage training datasets.
- Governance & Risk Management
- Enforce model governance policies, including risk management and audit trails.
- Monitor and optimize ML environments for performance and cost efficiency.
- API & Infrastructure Enablement
- Implement secure API endpoints for model consumption.
- Support containerization and orchestration (Docker, Kubernetes).
Required Skills & Experience
Technical Expertise- 3+ years in ML Ops or related engineering roles.
- Proficiency in CI/CD tools, cloud ML platforms, and scripting languages (Python, Bash).
- Experience with containerization (Docker), orchestration (Kubernetes), and API design.
- Collaboration & Communication
- Strong ability to work across data science, engineering, and IT teams.
- Tools & Technologies
- Familiarity with model governance, risk management, and monitoring frameworks.
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