Description

You will lead the design, implementation, and maintenance of scalable ML infrastructure solutions.

Responsibilities

• Design and implement scalable ML infrastructure solutions.
• Optimize model deployment workflows in collaboration with data science and ML teams.
• Develop and manage CI/CD pipelines to automate deployment.
• Architect and implement containerized environments using Docker and Kubernetes.
• Ensure infrastructure security, reliability, and cost-efficiency across cloud platforms.

Required Skills

• 5+ years of experience as an ML or Infrastructure Engineer focused on ML infrastructure.
• Strong expertise in Python and Java.
• Hands-on experience with cloud platforms, preferably GCP; AWS and Azure experience is valuable.
• Proficiency in containerization technologies including Docker and Kubernetes.
• Experience with Infrastructure as Code (IaC), specifically Terraform.
• Solid understanding of DevOps principles and CI/CD pipeline management.
• Familiarity with ML frameworks like TensorFlow and PyTorch, and libraries like scikit-learn.
• Knowledge of big data processing tools such as Apache Spark and Hadoop is highly preferred.

Preferred Skills

• Experience with monitoring and logging tools like Prometheus, Grafana, or ELK stack.
• Demonstrated experience leading complex infrastructure projects.

Key Skills

Education

Bachelor's or Master's degrees