Hiring For computer vision engineer

Computer Vision Engineer Developer

Experience: 520 Years
Location: PAN India
Employment Type: Full-Time
Job Type: AI / Machine Learning / Computer Vision

Role Overview

We are looking for experienced Computer Vision Engineers / Developers to design, develop, optimize, and deploy AI-driven computer vision solutions for real-world applications.

The role involves working on object detection, object tracking, image/video recognition, classification, segmentation, counting, OCR, visual inspection, and video analytics. The ideal candidate should have strong hands-on experience in deep learning, computer vision algorithms, model development, dataset preparation, and production deployment.

Candidates across 5–20 years of experience are welcome, with responsibilities ranging from hands-on development to technical leadership and architecture.

Key Responsibilities

• Design and develop computer vision and deep learning pipelines for image and video-based applications.
• Develop solutions for object detection, classification, recognition, tracking, segmentation, and counting.
• Work with images, videos, CCTV streams, edge devices, and real-time vision systems.
• Build, train, fine-tune, and evaluate deep learning models.
• Work with frameworks such as PyTorch, TensorFlow, OpenCV, ONNX, and YOLO.
• Prepare, clean, annotate, augment, and validate large-scale image/video datasets.
• Implement data preprocessing, feature extraction, model inference, and post-processing pipelines.
• Experiment with CNNs, Vision Transformers (ViT), Transformers, foundation models, and multimodal models.
• Optimize models for accuracy, latency, memory consumption, and real-time inference.
• Convert and optimize models using ONNX, TensorRT, TorchScript, or similar technologies.
• Develop solutions for real-world challenges such as occlusion, lighting variation, camera angle changes, motion blur, and varying object sizes.
• Evaluate models using appropriate metrics such as Precision, Recall, F1-score, mAP, IoU, ROC-AUC, and tracking metrics.
• Integrate computer vision models with backend services, APIs, and enterprise applications.
• Support deployment of AI models in cloud, on-premises, and edge environments.
• Work closely with Data Scientists, ML Engineers, Software Engineers, and Product teams.
• Troubleshoot model performance and production issues.
• Maintain technical documentation, experiment results, model versions, and deployment artifacts.

Required Technical Skills

Programming

• Strong proficiency in Python.
• Good understanding of C++ is an advantage.
• Experience with NumPy, Pandas, SciPy and related libraries.

Computer Vision

• OpenCV
• Image processing
• Video processing
• Object detection
• Object tracking
• Image classification
• Image segmentation
• OCR
• Pose estimation
• Feature extraction
• Visual similarity / image matching

Deep Learning

Strong knowledge of:

• CNN architectures
• ResNet / EfficientNet / MobileNet
• YOLO family
• Faster R-CNN / Mask R-CNN
• Vision Transformers
• Transformers
• Siamese / metric-learning approaches
• Segmentation models
• Transfer learning
• Model fine-tuning

AI / Foundation Models

Exposure to one or more of:

• Vision Foundation Models
• CLIP
• SAM / Segment Anything
• Vision-Language Models
• Multimodal AI
• Generative AI for vision
• Synthetic data generation
• LLM + Vision integration

ML Engineering / Deployment

• Model serving and inference pipelines
• REST APIs / FastAPI
• Docker / Kubernetes
• CI/CD
• MLflow or equivalent model lifecycle tools
• ONNX / TensorRT
• GPU-based inference
• AWS / Azure / GCP
• Edge AI deployment

Dataset & Model Development

• Experience with image/video data collection and preprocessing.
• Understanding of annotation tools and dataset management.
• Experience handling class imbalance, noisy labels, and dataset bias.
• Data augmentation and synthetic data generation.
• Train/validation/test dataset strategy.
• Model benchmarking and performance optimization.
• Experience with production-scale datasets is preferred.

Senior-Level Expectations – 10–15 Years

• Own end-to-end computer vision solutions from problem definition through production deployment.
• Design scalable CV/AI architectures.
• Lead model selection, experimentation, optimization, and deployment.
• Mentor junior and mid-level engineers.
• Define technical standards and development best practices.
• Work with stakeholders to translate business problems into computer vision solutions.
• Drive POCs and transition successful solutions into production.
• Improve model accuracy, latency, scalability, and reliability.

Lead / Architect Expectations – 15–20 Years

• Define the overall Computer Vision / AI technical architecture.
• Lead large-scale computer vision and AI initiatives.
• Design reusable CV platforms, frameworks, and production pipelines.
• Evaluate emerging technologies, foundation models, and AI architectures.
• Drive technical strategy for vision AI, multimodal AI, and intelligent video analytics.
• Lead architecture reviews, technical decisions, and engineering standards.
• Mentor senior engineers and technical leads.
• Collaborate with business, product, engineering, and data science leadership.
• Establish best practices around MLOps, model governance, scalability, security, and responsible AI.

Good to Have

• Experience with NVIDIA GPUs, CUDA, TensorRT.
• Experience with real-time video analytics.
• Experience with edge AI / embedded vision.
• Experience with autonomous systems, robotics, retail, manufacturing, surveillance, healthcare, or industrial vision.
• Knowledge of 3D computer vision / LiDAR / depth cameras.
• Experience with synthetic data platforms.
• Experience with multimodal / Vision-Language Models.
• Publications, patents, or contributions to open-source AI/CV projects.

Education

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, Computer Engineering, or a related field.
• Strong practical experience may be considered in lieu of an advanced degree.

Core Competencies

Python | OpenCV | PyTorch | TensorFlow | YOLO | Object Detection | Object Tracking | Image Classification | Image Segmentation | Deep Learning | Computer Vision | Transformers | Vision Transformers | OCR | Foundation Models | Synthetic Data | Vision-Language Models | ONNX | TensorRT | MLOps | Cloud | Edge AI | GPU

Experience Range

• 5–8 Years: Senior Computer Vision Engineer / Developer
• 8–12 Years: Lead Computer Vision Engineer
• 12–15 Years: Principal / Senior Lead – Computer Vision
• 15–20 Years: Computer Vision Architect / AI Architect / Technical Lead