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AI Computer Vision Engineer India Remote Role Overview We are looking for an experienced AI Computer Vision Engineer to join our project team and support the design, development, integration, and deployment of AI-powered computer vision solutions. The ideal candidate will have strong hands-on experience in Computer Vision, Deep Learning, Python, image/video analytics, and AI model development, with the ability to take models from experimentation and prototyping through production deployment. The role will involve developing computer vision models for image and video analytics, integrating AI solutions with enterprise applications, and optimizing model performance, scalability, and reliability for production environments. Key Responsibilities Design, develop, train, and deploy Computer Vision and Deep Learning models for real-world business applications.Develop AI solutions for image and video analytics, including object detection, classification, segmentation, tracking, OCR, and related use cases.Build and optimize models using frameworks such as PyTorch and/or TensorFlow.Develop image and video processing pipelines using Python and OpenCV.Implement and fine-tune object detection models using YOLO, Faster R-CNN, and similar architectures.Develop solutions for image classification, semantic/instance segmentation, OCR, pose estimation, and facial analysis.Implement object tracking and video analytics using technologies such as ByteTrack and DeepSORT.Work with modern vision architectures, including Vision Transformers (ViT), Swin Transformer, SAM 2, and CLIP.Explore and implement Generative AI and synthetic-data techniques for computer vision model development and data augmentation.Integrate AI/Computer Vision models with enterprise applications, APIs, and production systems.Optimize model accuracy, inference performance, latency, memory utilization, and scalability.Prepare datasets, perform data preprocessing, augmentation, annotation analysis, and model evaluation.Troubleshoot model and deployment issues and continuously improve production performance.Collaborate with software engineers, AI/ML engineers, architects, and project stakeholders to deliver production-ready solutions.Document model architecture, technical implementation, performance metrics, and deployment processes.Required Technical Skills 36 years of relevant experience in Computer Vision / AI / Deep Learning. Experience may be flexible based on the candidate's overall profile.Strong hands-on experience in Computer Vision and Deep Learning.Strong Python programming skills.Experience with OpenCV and image/video processing.Hands-on experience with YOLO and object detection frameworks.Strong experience with TensorFlow and/or PyTorch.Practical knowledge of:Object DetectionImage ClassificationImage SegmentationOCRObject TrackingVideo AnalyticsUnderstanding of model training, validation, evaluation, optimization, and deployment.Good understanding of computer vision algorithms, neural networks, CNNs, transformers, and deep learning concepts.Preferred Technical Exposure Candidates with experience in the following technologies will be strongly preferred: Object Detection YOLOFaster R-CNNImage Classification Vision Transformer (ViT)Swin TransformerPose Estimation YOLO PoseMediaPipeFace Analysis FaceNetObject Tracking ByteTrackDeepSORTAction Recognition MoViNetI3DGenerative Vision & Data Synthetic dataset generationData augmentationSynthetic data pipelinesFoundation Vision Models SAM 2CLIPVision-Language Models (VLMs) Hands-on experience with VLMs will be an added advantage.Deployment & Infrastructure Exposure to production deployment and MLOps practices is preferred, including: DockerKubernetesREST APIs / microservicesModel serving and inference optimizationCloud-based AI/ML deploymentGPU-based model inferenceCI/CD and production model deployment practicesEducation Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Electronics, Engineering, or a related technical discipline. Equivalent practical experience with a strong Computer Vision portfolio will also be considered. Location India Remote Preferred Location: Noida / Delhi NCR Candidates from other locations in India with strong relevant experience may also be considered. Experience 36 years, flexible based on technical expertise, project experience, and overall candidate profile. Ideal Candidate Profile The ideal candidate is a hands-on Computer Vision Engineer who can work across the complete AI development lifecyclefrom data preparation and model development to optimization, API integration, and production deployment. Candidates with demonstrable experience building and deploying real-world Computer Vision applications, particularly involving image/video analytics and modern vision models, will be preferred. Engagement Employment Type: Project-Based / Contractual Work Mode: Remote Location: .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.