Machine Learning Infrastructure Engineer

Responsibilities:

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

Requirements:

• Experience: 3+ years in infrastructure engineering, DevOps, or similar roles, with a focus on supporting machine learning workflows in production.
• Technical Skills: 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.