Senior ML Infrastructure Engineer

RESPONSIBILITIES

• Design, Build, and Maintain ML Pipelines: Develop and optimize end-to-end machine learning pipelines, including data ingestion, model training, validation, deployment, and monitoring.
• Implement Continuous Integration/Continuous Deployment (CI/CD) for ML Models: Establish robust CI/CD processes to automate the testing, deployment, and monitoring of machine learning models in production environments.
• Build and Own Production Infrastructure for Serving ML Models: Design, deploy, and maintain the production infrastructure necessary for real-time and batch serving of machine learning models, ensuring high availability, scalability, and reliability.
• Build and Own the Feature Store: Design, implement, and manage the feature store to ensure efficient and scalable storage, retrieval, and versioning of features used in machine learning models, enabling consistent and reusable feature engineering across teams.
• Collaborate with Data Scientists and Engineers: Work closely with data scientists, data engineers, and software engineers to ensure seamless integration of ML models into production systems, aligning models with business goals.
• Monitor and Optimize Model Performance: Implement monitoring solutions to track the performance of ML models in production, identifying and addressing any issues such as data drift, model degradation, or system bottlenecks.
• Ensure Scalability and Reliability: Design and implement scalable and reliable ML infrastructure, leveraging cloud platforms, containerization, and orchestration tools like Kubernetes and Docker.
• Automate Data and Model Management: Develop automated solutions for version control, model registry, and experiment tracking to manage the lifecycle of ML models efficiently.
• Optimize Resource Utilization: Manage and optimize the use of computational resources, such as GPUs and cloud instances, to balance performance with cost-effectiveness.
• Conduct Root Cause Analysis and Troubleshooting: Diagnose and resolve issues in ML pipelines, including debugging data, code, and model performance problems.
• Document Processes and Systems: Create and maintain comprehensive documentation of ML pipelines, deployment processes, and operational workflows to ensure knowledge sharing and continuity.

DESIRED SKILLS:

• Bachelor degree in computer science, engineering or related field
• 5+ years of experience in ML Infrastructure or ML engineering. 
• Hands-on and expertise experience  in: building and maintaining ML pipelines, building and managing scalable ML production infrastructure, and AWS or other major cloud services.
• Strong knowledge of CI/CD practices for ML models.
• Familiarity with DevOps principles and tools.
• Familiarity with TensorFlow, PyTorch, or similar frameworks.
• Proficient in Python and Java (or Scala).
• Excellent communication skills.
• Move fast, be a team player, and kind.