Computer Vision Engineer – Detection & Pose / Movement

equirement  Computer Vision Engineer – Detection & Pose / Movement Analysis
Location: Remote
Engagement Type: Contract
Experience Required: 3–5 Years

About the Role
We are seeking a highly skilled Computer Vision Engineer specializing in real-time detection, pose estimation, and movement analysis to design and deliver robust vision-based intelligence systems. The ideal candidate will be responsible for building scalable computer vision pipelines capable of detecting people and objects, analyzing human movement patterns, and tracking individuals across frames in real-world environments.
This role focuses on developing production-grade vision systems that operate reliably across varying lighting conditions, camera angles, occlusions, and live video feeds while maintaining high accuracy, scalability, and low-latency performance.
Key Responsibilities
Design and implement real-time computer vision pipelines for person and object detection.
Develop advanced pose estimation and movement analysis models to interpret body posture, gestures, and motion patterns.
Build multi-object tracking systems to accurately track individuals across video frames while maintaining identity persistence.
Optimize computer vision models for low-latency inference suitable for edge and near-edge deployments.
Ensure robust system performance across varying lighting conditions, camera angles, occlusions, and real-world environments.
Integrate computer vision models with video ingestion, streaming, and processing frameworks.
Evaluate and benchmark models using real-world datasets and live video feeds.
Collaborate with cross-functional teams to align vision models with business objectives and operational requirements.
Continuously improve detection accuracy, pose estimation, and tracking stability using state-of-the-art techniques.
Troubleshoot and optimize production vision systems for performance, scalability, and reliability.

Required Qualifications
Bachelor's degree in Computer Science, Artificial Intelligence, Electronics, Robotics, or a related discipline.
3–5 years of professional experience in Computer Vision Engineering or Applied AI development.
Strong expertise in Computer Vision and Deep Learning techniques.
Hands-on experience with object detection models such as YOLO, Faster R-CNN, and SSD.
Experience working with pose estimation frameworks including OpenPose, HRNet, and MediaPipe.
Practical experience with multi-object tracking algorithms such as DeepSORT, ByteTrack, and OC-SORT.
Strong programming skills in Python.
Experience with deep learning frameworks such as PyTorch and/or TensorFlow.
Hands-on experience building real-time video processing pipelines using OpenCV, GStreamer, and/or FFmpeg.
Strong understanding of image processing, feature extraction, GPU acceleration, model optimization, and performance tuning.
Experience evaluating computer vision models using metrics such as precision, recall, tracking accuracy, latency, and inference performance.
Experience working with live video feeds, real-world datasets, and production deployment scenarios.

Preferred Qualifications
Experience deploying models on edge devices using TensorRT, ONNX, and quantization techniques.
Experience optimizing inference performance for real-time applications.
Exposure to edge deployment and hardware acceleration.
Experience balancing trade-offs between accuracy, latency, performance, and cost.
Familiarity with distributed video processing and scalable AI architectures.
Experience building production-grade AI-powered computer vision systems.