Job Description

Roles & Responsibilities

Building the Engine: Architecting production-ready ML pipelines from scratch from training all the way to deployment.

Pushing Limits: Optimizing workloads using CUDA, cuDNN, and Nvidia TensorRT to make sure everything runs lightning-fast.

The Heavy Lifting: Doing the deep-dive data analysis and feature engineering needed for top-tier datasets.

Leading the Pack: Mentoring junior engineers, heading up code reviews, and making sure we re always following best practices.

Collaborating: Working closely with stakeholders to turn business needs into technical wins.

Staying Sharp: Keeping us on the cutting edge by staying obsessed with the latest AI/ML advancements.

Desired Candidate Profile

5+ years of hands-on experience developing and deploying ML models.

A pro with Python, TensorFlow, and PyTorch.

Experience with tools like MLflow or Kubeflow.

Advanced skills in Docker, REST APIs, and Git.

Someone who knows their way around Nvidia Nsight, PyTorch Profiler, or Tensorboard for optimization.

Experience deploying AI solutions on AWS, GCP, or Azure.

Strong leadership vibes and the ability to explain "techy" things to non-techy people.

A Bachelor s or Master s in Computer Science, Data Science, or a related field.

Relocation support will be provided.