Computer Vision Engineer

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.