AI Engineer

Job Summary

We are seeking a highly skilled and motivated MLOps Engineer with a strong background in computer vision. In this role, you will be responsible for the full lifecycle of our machine learning models, from development and optimization to deployment and scaling. You will build and maintain the infrastructure that allows our cutting-edge computer vision algorithms to run reliably and efficiently in production. The ideal candidate will have a deep understanding of both MLOps principles and 3D computer vision, with hands-on experience in containerization, model optimization, and scalable systems.

Responsibilities

• Design, build, and maintain robust, scalable, and automated MLOps pipelines for model training, evaluation, and deployment (CI/CD for ML).
• Containerize machine learning applications using Docker for scalable and reproducible deployments.
• Deploy and manage ML models at scale.
• Optimize deep learning models for inference performance, including techniques like quantization, pruning, and distillation.
• Work with and extend state-of-the-art AI models for tasks such as:
  • Depth estimation and 6D object pose estimation.
  • Image and video segmentation.
  • Dense point tracking and feature matching.
• Develop and maintain monitoring systems to track model performance, detect data drift, and ensure the reliability of production systems.
• Collaborate with AI researchers and software engineers to transition models from research to production.
• Manage and optimize pipelines for processing large-scale 3D data, including point clouds, LiDAR, and stereoscopic imagery.
• Apply a strong mathematical understanding of spatial transformations, rigid body rotations, and coordinate frame alignment to ensure algorithmic integrity in production.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field.
• Proven experience in an MLOps, DevOps, or similar role with a focus on machine learning.
• Strong programming skills in Python and/or C++.
• A portfolio of projects or publications in the field of computer vision or MLOps.
• Hands-on experience with model optimization techniques (quantization, etc.) and frameworks (e.g., TensorRT, ONNX Runtime).
• Hands-on experience with containerization technologies.
• Experience with CI/CD tools (e.g., Jenkins, GitLab CI, CircleCI) and version control (Git).
• Solid understanding of general computer vision and 3D computer vision principles.
• Experience with deep learning frameworks such as PyTorch or TensorFlow.
• A strong mathematical foundation in spatial transformations, rigid body rotations, coordinate frame alignment, and triangulation.