Note : This is a full-time contractor role, and we require that they do not take up any other employment while working with us.
Role Overview
We are seeking a highly skilled and motivated Machine Learning / Computer Vision Engineer to join our AI team. The core focus of this role will be the end-to-end development and deployment of deep learning models for advanced visual understanding. This includes a strong emphasis on Computer Vision tasks such as Object Detection, Semantic Segmentation, and Instance Segmentation . You will be responsible for translating cutting-edge research into robust, scalable, production-grade systems in a Python-centric environment.
Key Responsibilities
- Model Design and Development : Research, design, implement, and optimize state-of-the-art deep learning models specifically for computer vision segmentation and object detection algorithms.
- ML Lifecycle Management : Own the full machine learning lifecycle, from data collection and annotation to training, evaluation, validation, and production deployment.
- Coding & Integration : Write clean, efficient, and well-documented production code in Python , utilizing key ML / CV libraries and frameworks.
- System Integration : Collaborate with software and platform engineers to seamlessly integrate computer vision capabilities into our core products and infrastructure.
- Performance Optimization : Evaluate model performance, benchmark speed and accuracy, and optimize models for inference latency and memory consumption on target hardware (cloud or edge).
- Research & Innovation : Stay abreast of the latest academic and industry advancements in deep learning and computer vision to propose and implement innovative solutions.
- Collaboration : Work closely with data scientists, software developers, and product managers to define requirements and deliver high-impact features.
Required Qualifications
Experience : A minimum of 3+ years of professional experience in a Machine Learning Engineer, Computer Vision Engineer, or similar role.Programming : Strong proficiency in Python and its scientific computing stack (e.g., NumPy, Pandas).Computer Vision : Proven practical experience with computer vision algorithms and deep learning techniques for Object Detection (e.g., YOLO, Faster R-CNN) and Segmentation (Semantic or Instance).Deep Learning Frameworks : Expertise in at least one major deep learning framework ( PyTorch or TensorFlow / Keras ).Tooling : Hands-on experience with computer vision libraries such as OpenCV .Foundational Knowledge : Solid understanding of machine learning principles, neural network architectures (especially CNNs), and image processing fundamentals.Education : Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related technical / quantitative field.Proficiency in modern code management tools, especially Git.Preferred Qualifications (Nice-to-Haves)
Experience with MLOps practices and tools (e.g., Docker, Kubernetes, experiment tracking, model serving).Familiarity with cloud computing services (AWS, Google Cloud Platform, or Azure) for model training and deployment.Experience in optimizing models for performance and size (e.g., quantization, pruning, use of TensorRT / ONNX).