Computer Vision Engineer

Senior Vision AI Engineer

About the Role

We are looking for a Senior Vision AI Engineer to architect, lead, and deliver advanced Computer Vision, Deep Learning, and Perception solutions for real-time intelligent systems.

The ideal candidate should have strong technical leadership and hands-on engineering expertise, with proven experience building production-grade AI products. Experience in ADAS, autonomous systems, smart cameras, industrial vision, or Edge AI is preferred.

You will mentor engineers, influence architecture decisions, and work closely with cross-functional teams to build high-performance, scalable Vision AI pipelines.

Key Responsibilities

1. Vision AI & Deep Learning

• Design, develop, and optimize end-to-end Computer Vision pipelines, including pre-processing, inference, and post-processing.
• Build and deploy real-time models for object detection, object tracking, image segmentation, calibration, and image classification.
• Train, fine-tune, and evaluate deep learning models using PyTorch, TensorFlow, and ONNX.
• Develop robust image and video processing algorithms, including feature extraction and classical Computer Vision techniques.

2. Edge AI & Embedded Deployment

• Optimize and deploy AI models on edge platforms such as NVIDIA Jetson and Qualcomm platforms, using DSP/GPU/NPU accelerators.
• Convert and optimize models using TensorRT, ONNX Runtime, QNN, quantization, pruning, and other optimization techniques.
• Develop high-performance C++ (14/17/20) modules for embedded Computer Vision applications.

3. System Design & Architecture

• Architect scalable and modular Computer Vision systems using OOAD, SOLID principles, design patterns, and UML.
• Define data flows, pipeline architecture, CPU/GPU/NPU execution strategies, and system integration approaches.
• Collaborate with hardware, systems, and product teams to ensure real-time performance, reliability, and scalability.

4. Technical Leadership & Mentoring

• Mentor junior and mid-level engineers through code reviews, architecture discussions, and best engineering practices.
• Take technical ownership of feature modules, delivery quality, and project timelines.
• Drive innovation by evaluating new research papers, frameworks, and Computer Vision techniques.

5. Deployment & MLOps

• Develop production-ready inference modules, CI/CD pipelines, and testing frameworks for Vision AI models.
• Define and evaluate KPIs, benchmark model and system performance, and optimize solutions to meet product SLAs.
• Support deployments for global clients and collaborate effectively with onshore and offshore teams.

Required Skills & Experience

Core Technical Expertise

• 3+ years of hands-on experience in Computer Vision and Deep Learning.
• Strong programming skills in C++ (11/14/17) and Python.
• Strong hands-on experience with PyTorch, TensorFlow, Keras, and ONNX.
• Strong understanding of image processing, OpenCV, video analytics, and classical Computer Vision techniques such as SIFT and SURF.
• Experience with deep learning architectures such as CNNs, ResNet, YOLO, VGG, segmentation networks, and sequence models (LSTM/GRU).
• Strong understanding of model training, evaluation, optimization, and deployment.

Edge AI & Performance Optimization

• Hands-on experience with TensorRT, ONNX Runtime, model quantization (INT8/FP16), pruning, and model acceleration.
• Experience working with GPU, DSP, or NPU execution backends and hardware-aware optimization techniques.
• Strong understanding of performance optimization for real-time and low-latency AI applications.

System Engineering

• Strong knowledge of data structures, algorithms, memory optimization, multithreading, and low-latency systems.
• Experience with UML, OOAD, and software design patterns such as Factory, Strategy, and Observer.
• Ability to design modular, scalable, and maintainable software architectures.

Leadership

• Experience mentoring engineers or leading technical/feature modules.
• Ability to conduct effective code reviews, establish engineering best practices, and drive technical excellence.

Good to Have

• Experience building production-grade AI products in a product-based environment.
• Familiarity with MLOps, Docker, GitLab CI/CD, and cloud deployment.
• Exposure to ADAS perception, autonomous driving stacks, robotics, or industrial automation.
• Experience working with real-time camera/video processing systems.