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.

Positive 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.

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