Remote Machine Learning Infrastructure Engineer

Summary

Join Flip.shop, where innovation meets social commerce revolution! We're seeking a Machine Learning Infrastructure Engineer to design, build, and optimize infrastructure for deploying, monitoring, and maintaining machine learning models in production environments. This role offers the opportunity to create scalable, production-level systems that support real-time recommendations and drive business growth.

Requirements

• 3+ years in infrastructure engineering, DevOps, or similar roles, with a focus on supporting machine learning workflows in production
• Strong proficiency in cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), CI/CD pipelines, and infrastructure-as-code tools (Terraform, Ansible)
• Experience with SageMaker is a bonus
• ML Workflow Knowledge: Experience working with machine learning frameworks (TensorFlow, PyTorch, or similar) and familiarity with MLOps practices
• Performance & Scalability: Proven track record of optimizing infrastructure for performance, scalability, and reliability in production environments
• Collaboration: Strong teamwork skills, with the ability to partner with ML engineers and data scientists to streamline workflows
• Communication: Ability to communicate complex infrastructure solutions to technical and non-technical stakeholders
• Problem-Solving: Passion for solving infrastructure challenges that support real-time machine learning at scale

Responsibilities

• Design and implement scalable infrastructure for deploying, monitoring, and maintaining machine learning models in production environments
• Build tools to automate workflows for model training, testing, and deployment, ensuring that machine learning models can move quickly from development to production
• Leverage cloud platforms to create efficient, scalable systems for large-scale machine learning workloads
• Ensure the infrastructure supports high-performance model inference at scale, with a focus on minimizing latency and maximizing throughput
• Work closely with data scientists, machine learning engineers, and DevOps teams to create seamless integration between development and production environments
• Build robust monitoring systems to track model performance and infrastructure health, ensuring reliability and uptime of machine learning services
• Implement best practices in infrastructure security, data privacy, and compliance, particularly when handling sensitive user data