Deep Learning Engineer

Job Description

Job Title: Computer Vision / Deep Learning Engineer Location: Hyderabad, India Experience: Minimum 2 years Employment Type: Full-time Position Overview: We are seeking a Computer Vision / Deep Learning Engineer to join our innovative and fast-paced R&D team. In this role, you will develop cutting-edge vision algorithms for real-time military applications . From early-stage prototyping to embedded system deployment, you will help drive solutions that perform reliably in dynamic and challenging environments. Key Responsibilities: Design and implement deep learning and traditional computer vision algorithms for object detection, segmentation , and multi-object tracking . Translate academic research into scalable, real-world applications for embedded and resource-constrained systems . Develop complete pipelines including data acquisition, model training, evaluation , and deployment . Collaborate with embedded engineers and robotics teams to fine-tune models for field conditions. Participate in agile development practices —sprint planning, code reviews, and integration testing. Continuously optimize models for accuracy, robustness , and low-latency inference on edge devices. Core Technical Skills: Strong programming skills in Python and C++ . Proficient in PyTorch, TensorFlow, OpenCV , and other related libraries. Solid knowledge of image classification, object detection, segmentation, and tracking techniques. Experience with embedded AI deployments on platforms like NVIDIA Jetson, ARM Cortex , etc. Familiarity with performance optimization , quantization , and model pruning for edge inference. Experience with version control (Git) and CI/CD pipelines . Required Qualifications: Bachelor’s or Master’s degree in Computer Science, Electronics, Artificial Intelligence , or a related field. Minimum 2 years of hands-on experience in computer vision and deep learning . Strong understanding of model evaluation metrics , dataset management , and reproducibility best practices . Show more Show less