Machine Learning Ops Engineer

Summary

Join our team as an ML Ops Engineer and be responsible for designing, building, and maintaining scalable machine learning pipelines. You will deploy models to production, manage infrastructure, implement CI/CD, and ensure reliability and scalability. The role involves collaboration with data scientists, performance optimization, and managing containers and APIs. You will also be responsible for monitoring, security, and implementing disaster recovery plans. This position reports to the Manager – AI and requires a Bachelor's or Master's degree in a related field and 4+ years of experience.

Requirements

• Bachelor’s or master’s degree in computer science, Engineering, Data Science, or a related field
• 4 years of proven experience as an ML Ops Engineer or similar role in a production environment
• Experience with Azure cloud platform
• Experience with containerization technologies (Docker, Kubernetes)
• Experience with API management tools (Kong)
• Strong programming skills in Python
• Proficiency in CI/CD tools
• Familiarity with machine learning frameworks (TensorFlow, PyTorch)
• Strong understanding of DevOps practices and principles
• Excellent problem-solving skills and attention to detail
• Strong communication and collaboration skills

Responsibilities

• Design, build, and maintain scalable ML pipelines to ensure efficient data processing and model deployment
• Develop and manage APIs to support machine learning models and services
• Ensure seamless integration between machine learning models and external applications
• Utilize API management tools to monitor and secure API calls, enforcing access control and data protection measures
• Deploy machine learning models to various environments, including testing and production, ensuring seamless integration and functionality
• Ensure the reliability, availability, and scalability of ML pipelines by implementing robust monitoring and alerting systems
• Provision pipeline operations effectively, managing resources such as compute, storage, and networking to optimize performance and cost-efficiency
• Develop and maintain CI/CD pipelines tailored for ML models and applications
• Automate the build, test, and deployment processes
• Utilize containerization technologies such as Docker and Kubernetes for deploying ML models, ensuring consistency and portability across environments
• Manage and orchestrate containers effectively to optimize resource utilization and maintain scalability
• Implement comprehensive monitoring and logging solutions to track the performance of ML models and pipelines, enabling proactive issue detection and resolution
• Set up robust alerting systems to detect and respond to issues and anomalies promptly, minimizing downtime and performance degradation
• Ensure compliance with security standards and regulations, implementing measures to protect data privacy and model security
• Continuously monitor and optimize the performance of ML models and infrastructure, identifying and resolving bottlenecks to improve system efficiency
• Respond to and resolve incidents related to ML operations promptly
• Set up and manage both cloud and on-premises infrastructure to support ML operations
• Optimize models and infrastructure for performance and scalability in production environments, ensuring efficient and reliable operations
• Manage resource allocation to ensure cost-effective operations
• Develop scripts and automation tools to streamline ML operations, automating repetitive tasks to improve operational efficiency
• Implement backup and disaster recovery plans for ML models and data
• Ensure data and model availability in case of failures
• Conduct root cause analysis and implement preventive measures to mitigate future occurrences
• Collaborate closely with data scientists and engineers throughout the ML lifecycle, from model development, and testing to deployment and maintenance
• Collaborate with data scientists and AI researchers to develop and test machine learning models
• Provide support and guidance on best practices for ML operations, facilitating effective teamwork and knowledge sharing
• Implement best practices for model versioning, testing, and validation

Preferred Qualifications

AWS experience