Computer Vision Engineer

Senior Computer Vision Engineer – Vision Systems \& Inference

• *Experience:** 5\+ years

Role Overview

We are looking for an experienced Computer Vision Engineer who has built and deployed **real\-world computer vision analytics systems** involving object detection, segmentation, tracking, OCR, video analytics, and multi\-camera processing.

The ideal candidate should have strong computer vision fundamentals along with hands\-on experience designing **inference architectures, high\-throughput data pipelines, model integration, system optimization, and production deployment**.

This is primarily an **applied computer vision engineering role**, with emphasis on building reliable and scalable vision systems.

Key ResponsibilitiesComputer Vision \& Model Implementation

· Implement and fine\-tune computer vision models for **object detection, segmentation, tracking, OCR, and video analytics**.

· Work extensively with architectures such as **YOLO and other modern detection/segmentation models**.

· Integrate and adapt pretrained models for application\-specific datasets and requirements.

· Evaluate models using appropriate accuracy, robustness, and performance metrics.

Multi\-Camera \& Object Tracking

· Design and implement **multi\-object and multi\-camera tracking systems**.

· Work with tracking approaches such as ByteTrack, DeepSORT, ReID\-based tracking, or similar techniques.

· Handle object association, identity consistency, camera streams, and temporal information in real\-world environments.

OCR \& Video Analytics

· Work with modern OCR pipelines for applications such as ANPR, text detection, and recognition.

· Build complete video\-processing workflows combining detection, tracking, OCR, segmentation, and business analytics.

Inference Architecture \& Performance Optimization

· Design efficient inference pipelines for **real\-time and high\-throughput video processing**.

· Optimize GPU/CPU utilization, model execution, preprocessing, post\-processing, and data movement.

· Use **multiprocessing, multithreading, asynchronous processing, queues, batching, and pipeline parallelism** where appropriate.

· Identify and resolve inference bottlenecks involving compute, memory, I/O, or system architecture.

Data Pipeline Engineering

· Build scalable pipelines capable of processing **large image/video datasets and multiple camera streams**.

· Develop efficient preprocessing, augmentation, validation, storage, and data\-ingestion workflows.

· Design systems that can reliably process continuous or large\-volume visual data.

Deployment

· Deploy computer vision applications across **GPU servers, cloud infrastructure, and edge platforms such as NVIDIA Jetson**.

· Optimize models using approaches such as **TensorRT, ONNX, FP16/INT8 inference, quantization, and batching** where applicable.

· Build maintainable production systems around trained computer vision models.

Computer Vision Fundamentals

· Apply strong knowledge of both **classical computer vision and deep\-learning\-based vision techniques**.

· Understand image processing, geometric transformations, camera systems, feature extraction, filtering, image enhancement, and related concepts.

· Understand model training, transfer learning, loss functions, optimization, hyperparameter tuning, and model evaluation.

Required Skills

· 5\+ years of hands\-on experience in Computer Vision / Machine Learning.

· Strong experience with **Python, OpenCV, PyTorch and/or TensorFlow**.

· Production experience with **YOLO or similar object detection and segmentation architectures**.

· Strong experience with **object tracking and video analytics systems**.

· Experience building or integrating **OCR pipelines**.

· Experience handling **multiple camera/video streams**.

· Strong understanding of **inference architecture and performance optimization**.

· Experience with **multiprocessing, multithreading, asynchronous pipelines, and GPU\-based inference**.

· Experience building pipelines for **large\-scale image/video datasets**.

· Good understanding of computer vision fundamentals and deep\-learning concepts.

· Experience fine\-tuning and adapting pretrained computer vision models.

Good to Have

· Experience with **multi\-camera multi\-object tracking (MTMC)** and ReID systems.

· Experience with segmentation architectures such as **U\-Net, U\-Net\+\+, SegFormer, or transformer\-based vision models**.

· Experience with depth maps, stereo vision, point clouds, or other **3D computer vision data**.

· Experience with **TensorRT, ONNX Runtime, CUDA, Triton Inference Server, or similar inference technologies**.

· Experience deploying models on **NVIDIA Jetson or other edge devices**.

· Exposure to distributed processing, streaming architectures, RTSP/CCTV systems, or large\-scale video analytics platforms.

· Experience profiling and optimizing end\-to\-end computer vision applications.

Education

B.Tech / M.Tech in Computer Science, Computer Engineering, Information Technology, Electronics, Electrical Engineering, or a related field from a reputed institution, or equivalent relevant industry experience.

kshama.raj@blueocean.systems

Experience

• Computer vision: 6 years (Preferred)

Work Location: In person