Computer Vision Engineer

Computer Vision Engineer 4+ Years Experience

Key Responsibilities

• Design, develop, and deploy Computer Vision and AI solutions for real-world applications.
• Develop and optimize object detection, classification, tracking, segmentation, and image/video analytics models.
• Work with YOLO (v8/v11), OpenCV, PyTorch, TensorFlow, and other deep learning frameworks.
• Develop real-time video analytics pipelines using CCTV, RTSP, IP cameras, and video streams.
• Implement multi-object tracking, counting, activity recognition, anomaly detection, and event detection.
• Perform data collection, preprocessing, annotation, augmentation, model training, validation, and optimization.
• Fine-tune pretrained models and improve accuracy, FPS, latency, and inference performance.
• Deploy computer vision models using FastAPI/Flask, REST APIs, Docker, and cloud platforms.
• Integrate AI models with databases, backend services, dashboards, and enterprise applications.
• Optimize models for GPU/CPU inference and edge deployment.
• Monitor deployed models and troubleshoot production issues.
• Collaborate with AI/ML engineers, backend/frontend developers, and business teams to deliver POCs and production solutions.

Required Technical Skills

• 4+ years of hands-on experience in Computer Vision / Deep Learning.
• Strong programming skills in Python.
• Strong experience with OpenCV, PyTorch and/or TensorFlow.
• Hands-on experience with YOLOv8/YOLOv11 or similar object detection frameworks.
• Good understanding of CNNs, object detection, segmentation, classification, tracking, and image processing.
• Experience with DeepSORT, ByteTrack, SORT, or similar tracking algorithms.
• Experience working with RTSP/IP cameras and real-time video processing.
• Knowledge of NumPy, Pandas, CUDA/GPU acceleration, and model optimization.
• Experience building and consuming REST APIs using FastAPI/Flask.
• Knowledge of Git, Linux, Docker, and basic CI/CD practices.
• Experience with AWS/Azure/GCP is an advantage.

Good to Have

• Experience with OCR, Face Recognition, PPE Detection, ANPR/ALPR, pose estimation, or behavioral analytics.
• Experience deploying models on NVIDIA GPUs, Jetson, or edge devices.
• Knowledge of ONNX, TensorRT, OpenVINO, or model quantization.
• Experience with MLOps and production AI deployment.
• Understanding of Generative AI/LLMs and multimodal AI is a plus.

Education

• Bachelor's/Master's degree in Computer Science, Artificial Intelligence, Data Science, Electronics, or a related field.

Candidate Profile

• Strong problem-solving and analytical skills.
• Ability to independently develop POCs and convert them into production-ready solutions.
• Good communication and teamwork skills.
• Ability to work with real-world datasets and handle model performance challenges.