Site Reliability Engineer

MUST HAVE EXP :

• Candidates should have moved from Devops to ML ops
• Candidates who are on GEN AI with strong ML ops would also be a fit but must have prior DevOps exp
• Candidates with ETL data pipelines with ML ops would also fit the role
• Strong Python knowledge is a must for this role and should be an individual contributor

About the role:

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.

Required skills

• 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.

Roles & 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.