ML Ops Engineer

Fractal Analytics

Posted: 5 months ago
Openings: 10
Applicants: 0

Job Description

We are seeking a skilled MLOps Platform Engineer to build and maintain a robust platform for the entire Machine Learning (ML) lifecycle. This role involves automating ML model training, testing, deployment, and monitoring, integrating seamlessly with our existing infrastructure to accelerate ML innovation.

Key responsibilities:

CI/CD for ML : Develop and manage CI/CD pipelines for ML model code and infrastructure, covering unit, integration, and deployment to all environments.
Automated ML Training : Design and implement pipelines for repeatable model training, automatic sweeps, data processing, and hyperparameter optimization.
- Include scheduling, queuing, and cost monitoring for training runs.
- Support training on cloud and on-premise GPU resources.
Model Monitoring : Establish tools and dashboards for continuous monitoring of deployed ML models, ensuring health, availability, and performance.
Platform Integration : Ensure the MLOps platform integrates with company workflows, data pipelines, and computing architecture.
MLOps Best Practices : Promote and implement best practices for reproducibility, version control, and governance across the ML lifecycle.

Skills:

Strong experience MLOps, DevOps, or ML Engineering, focusing on ML infrastructure
Programming: Strong proficiency in Python and ML frameworks ( TensorFlow, PyTorch, Scikit-learn ).
CI/CD & Orchestration: Experience with CI/CD tools (eg, Jenkins, GitLab CI, GitHub Actions) and workflow orchestrators (eg, Airflow).
Model Serving & De