Description

You will own the design, deployment, and scaling of fault-tolerant AI production systems, taking models from prototype to large-scale production.

This role is remote.

Responsibilities

• Design and implement complex, highly available ML solutions on AWS, Azure, or GCP.
• Lead code reviews, set coding standards, and manage the software development lifecycle.
• Build and maintain data pipelines using ETL processes, data warehousing, and Spark.
• Deploy scalable ML infrastructure using Kubeflow, MLflow, Docker, and Kubernetes.
• Set the technical vision and guide the engineering team.

Required Skills

• 10+ years of experience in machine learning engineering.
• Deep expertise in Python and scientific computing libraries (TensorFlow, PyTorch, scikit-learn, Pandas).
• Strong theoretical and practical understanding of machine learning algorithms and deep learning architectures.
• Proven experience with MLOps tools and cloud-native services (Kubeflow, MLflow, Docker, Kubernetes).
• Solid understanding of distributed computing frameworks (Spark) and data warehousing.
• Bachelor's or Master's degree in Computer Science.

Key Skills

Education

Bachelor's or Master's degrees