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Computer Vision Engineer
2 weeks ago
Role: Computer Vision Engineer
Key Responsibilities
• Lead the design and implementation of advanced computer vision algorithms.
• Architect, train, and optimize deep learning models for object detection, semantic
segmentation, and real-time inference across diverse environments.
• Collaborate with cross-functional engineering, product, and research teams to
integrate computer vision capabilities into scalable, production-grade systems.
• Drive data preprocessing strategies to ensure high-quality inputs for model training,
with a focus on robustness and generalizability.
• Conduct rigorous experimentation, benchmarking, and performance analysis to
continuously improve model accuracy and efficiency.
• Develop and optimize algorithms for low-latency, high-throughput inference
pipelines using GPU acceleration.
• Stay ahead of emerging trends in computer vision, deep learning, and edge
deployment—translating research into practical solutions.
• Mentor Interns and contribute to technical reviews, architecture decisions, and
roadmap planning.
Required Skills and Qualifications
• Expert-level proficiency in Python with deep understanding of algorithms and data
structures tailored to computer vision.
• Strong foundation in machine learning, neural networks, and image processing
techniques.
• Proficiency in OpenCV for image manipulation, filtering, edge detection, and
segmentation.
• Solid grasp of foundational deep learning architectures (e.g., CNNs, U-Nets,
ResNets) and their practical deployment.
• Experience working with multi-spectral or hyper-spectral data, especially for
detection and segmentation tasks.
• Familiarity with real-time inference frameworks such as ONNX Runtime and
TensorRT, including optimization for GPU-based deployment (Good to have).
• Good to have experience with PyTorch for building, training, and deploying deep
learning models.
• Experience integrating with Redis, RabbitMQ, and SQL databases for data
streaming and messaging.
• Proficient in Docker and CUDA frameworks for containerized model deployment
and GPU acceleration.
• Strong command of Linux environments, including scripting, debugging, and
performance tuning.
• Proven ability to solve complex problems independently and lead technical
initiatives within a team.
• Excellent communication skills for cross-functional collaboration and technical
documentation.