Description

You will design, implement, and maintain scalable end-to-end ML systems and MLOps pipelines. You own the lifecycle from evaluation and statistical modeling to deployment, monitoring, and optimization.

This role is on-site.

Responsibilities

• Design and implement scalable ML systems, including evaluation techniques, machine learning algorithms, and statistical modeling.
• Manage end-to-end deployment, metric generation, and continuous model monitoring.
• Optimize ML models using quantization, pruning, or speculative decoding to improve performance.
• Identify and resolve system bottlenecks to ensure efficient model execution and infrastructure utilization.
• Collaborate with teams to define problems, lead inter-team communication, and maintain effective technical documentation.

Required Skills

• 8+ years of experience with recent hands-on projects in the last 12 months.
• Strong proficiency in Java (algorithms, dependency/package management) and Python.
• Hands-on experience with TensorFlow or PyTorch.
• Proficiency with Docker and Kubernetes for containerization.
• Knowledge of DevOps principles, CI/CD pipelines, and Terraform.
• Experience with MLOps lifecycle management and model monitoring.
• Bachelor's degree in a relevant field.

Preferred Skills

• Experience with GCP ML tech stack and Infrastructure as Code.
• Knowledge of big data technologies such as Apache Spark or Hadoop.
• Experience with on-device ML system development.

Key Skills

Education

Bachelor's degree