Quince · 5 days ago

Senior ML Infrastructure Engineer - MLOps

United States

Full-time

Remote

Senior Level

$200K/yr - $250K/yr

5+ years exp

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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.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

ML InfrastructureML PipelinesAWSCI/CD for MLPythonJavaTensorFlowPyTorchDevOps principles