Computer Vision Engineer - Manufacturing Defects Analysis– Immediate Joiner

Job Title: Computer Vision Engineer - Manufacturing Defects Analysis

Location: Gurugram

Employment Type: Full-time

Department: Engineering / R&D / AI/ML Team

Key Responsibilities:

• Design, implement, and optimize computer vision and deep learning algorithms for automated detection of manufacturing defects (e.g., Scratches, Dents, holes, Surface finishing, Cosmetic stains, Dimensions, Cracks, Edge Chip off, Black & white spots, Misalignments, Labels & Signs, Puncher dots, foreign particles).
• Collect, annotate, analyze, and preprocess structured and unstructured image/video datasets including images, sensor streams, and production logs from manufacturing lines.
• Develop, validate, and deploy largescale machine learning and statistical models to detect, classify, and predict manufacturing defects.
• Collaborate with manufacturing, QA, and IT teams to integrate vision systems into production lines for comprehensive defect analysis.
• Conduct exploratory data analysis (EDA) to identify patterns, root causes, and risk factors of quality issues.
• Develop proof-of-concept demos and production-grade models & pipelines for real-time defect detection.
• Fine-tune models for accuracy, speed, and robustness in variable manufacturing environments.
• Present analytical findings to stakeholders and recommend actionable process improvements.
• Monitor and maintain deployed models, retraining and refining as new data becomes available.
• Document models, design choices, workflows, best practices and insights in a reproducible and scalable manner.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Robotics, or related field.
• 2+ years of hands-on experience in computer vision, image processing, multi-modal sensor fusion and deep learning, preferably in a manufacturing or industrial context.
• Proficiency in Python, with strong knowledge in libraries such as OpenCV, PyTorch, TensorFlow, or Keras. Building and evaluating machine learning models (classification, anomaly detection, time series, etc.).
• Experience designing & training CNNs and other architectures for visual inspection tasks.
• Familiarity with deployment on edge devices (e.g., NVIDIA Jetson, Intel Movidius) is a plus.
• Good understanding of manufacturing processes and common defects (preferred).
• Experience with data labelling, MLOps practices, database systems and data pipeline tools, Cloud platforms for data science work flows
• Strong problem-solving, team collaboration, and communication skills, with an ability to translate complex insights into actionable outcomes.

Application Process: Please submit your resume, cover letter, and any relevant portfolio or GitHub links to