Client | Sr. ML Platform Engineer
1 week ago
- Evaluate and select appropriate cloud services for each stage of the ML lifecycle.
- Design and implement the overall architecture of the MLOps platform.
- Set up automated pipelines for data preparation, model training, and deployment.
- Implement version control for code, data, and models to maintain reproducibility and traceability.
- Ensure the platform is scalable, secure, and compliant with relevant regulations.
- Provide tools and interfaces for data scientists to easily leverage the platform.
- Continuously optimize the platform for performance and cost-efficiency.
- Develop tools and interfaces for data scientists to easily utilize the platform's capabilities.
- Bridge the gap between data science and operations, enabling efficient development, deployment, and maintenance of ML models at scale.
Job Requirements:
- Bachelor’s/Master’s degree in Engineering, Computer Science, or equivalent experience.
- At least 10+ years of professional experience in building cloud-based applications, including ML platforms.
- Extensive expertise in cloud platforms like AWS, Google Cloud Platform, or Azure (Azure preferred).
- Proficiency in DevOps practices, including CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure as code.
- Strong understanding of ML workflows, model training, deployment processes, and data engineering pipelines.
- Advanced programming skills, particularly in Python, with a focus on ML applications.
- Experience in designing secure, scalable systems that adhere to regulatory compliance.
- Demonstrated ability to collaborate with cross-functional teams, including data scientists and software engineers.
Total Years Of exp: 10+ years experience mandatory
Recent Experience: 2-4 years in ML Ops.
Previous Experience: Remaining years in DevOps.
Preferred Background: DevOps professional transitioned into an ML Ops engineer.
Opportunity: Full time, 4 - 5 hours (PST overlap)
Notice Period: 2 weeks only
Engagement length: Full Time
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