19 Akshaya Business IT solutions Jobs
10-15 years
Remote
Machine Learning Platform Engineer - MLOps (10-15 yrs)
Akshaya Business IT solutions
posted 27d ago
Flexible timing
Key skills for the job
Job Description :
Turing is looking for people to join us in building ML platforms for our Fortune 500 customers. You will be a key member of the Turing GenAI delivery organization heading a team of other Turing engineers across different skill sets.
Responsibilities :
- 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.
- 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.
- This role is crucial in bridging the gap between data science and operations, enabling organizations to efficiently develop, deploy, and maintain machine learning models at scale.
Requirements :
- 10+ years of professional experience in building applications using cloud services.
- Prior experience in building Machine Learning platforms using cloud services.
- Cloud expertise : Deep knowledge of cloud platforms like AWS, Google Cloud Platform, or Azure, including their machine learning and data services (Azure preferred).
- DevOps skills : Experience with CI/CD pipelines, infrastructure as code, and containerization technologies like Docker and Kubernetes.
- Machine learning knowledge : Understanding of ML workflows, model training, and deployment processes.
- Data engineering : Familiarity with data pipelines, ETL processes, and data storage solutions.
- Software engineering : Strong programming skills, particularly in languages commonly used in ML like Python.
- System design : Ability to architect scalable, reliable systems that integrate various services.
- Automation : Expertise in automating workflows and processes across the ML lifecycle.
- Security and compliance : Knowledge of best practices for securing ML pipelines and ensuring regulatory compliance.
- Monitoring and logging : Experience setting up monitoring and logging for ML systems.
- Collaboration : Ability to work with data scientists, software engineers, and other stakeholders.
Functional Areas: Software/Testing/Networking
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