On-Site
Full-Time
Gurugram, Haryana
India
About the Role
Designation: AI/ML Engineer
Location: Gurugram
Experience: 3+ years
Budget: Upto 35 LPA
Industry: AI Product
Role and Responsibilities:
Model Development: Design, train, test, and deploy machine learning models using frameworks like Pytorch and TensorFlow, specifically for virtual try-on applications with a focus on draping and fabric simulation.
Task-Specific Modeling: Build models for tasks such as Natural Language Processing (NLP), Speech-to-Text (STT), and Text-to-Speech (TTS) that integrate seamlessly with computer vision applications in the virtual try-on domain.
Image Processing: Implement advanced image processing techniques including enhancement, compression, restoration, filtering, and manipulation to improve the accuracy and realism of draping in virtual try-on systems.
Feature Extraction & Segmentation: Apply feature extraction methods, image segmentation techniques, and draping algorithms to create accurate and realistic representations of garments on virtual models.
Machine Learning Pipelines: Develop and maintain ML pipelines for data ingestion, processing, and transformation to support large-scale deployments of virtual try-on solutions.
Deep Learning & Draping: Build and train convolutional neural networks (CNNs) for image recognition, fabric draping, and texture mapping tasks crucial to the virtual try-on experience.
AI Fundamentals: Leverage a deep understanding of AI fundamentals, including machine learning, computer vision, draping algorithms, and generative AI (Gen AI) techniques to drive innovation in virtual try-on technology.
Programming: Proficiently code in Python and work with other programming languages like Java, C++, or R as required.
Cloud Integration: Utilize cloud-based AI platforms such as AWS, Azure, or Google Cloud to deploy and scale virtual try-on solutions, with a focus on real-time processing and rendering.
Data Analysis: Perform data analysis and engineering to optimize the performance and accuracy of AI models, particularly in the context of fabric draping and garment fitting.
Continuous Learning: Stay informed about the latest trends and developments in machine learning, deep learning, computer vision, draping technologies, and generative AI (Gen AI), applying them to virtual try-on projects.
Skills Required:
Experience: Minimum of 5 years in Computer Vision Engineering or a similar role, with a focus on virtual try-on, draping, or related applications.
Programming: Strong programming skills in Python, with extensive experience in Pytorch and TensorFlow.
Draping & Fabric Simulation: Hands-on experience with draping algorithms, fabric simulation, and texture mapping techniques.
Data Handling: Expertise in data pre-processing, feature engineering, and data analysis to support high-quality model development, especially for draping and virtual garment fitting.
Deep Neural Networks & Gen AI: Extensive experience in working with Deep Neural Networks, Generative Adversarial Networks (GANs), Conditional GANs, Transformers, and other generative AI techniques relevant to virtual try-on and draping.
Advanced Techniques: Proficiency with cutting-edge techniques like Stable Diffusion, Latent Diffusion, InPainting, Text-to-Image, Image-to-Image models, and their application in computer vision and virtual try-on technology.
Algorithm Knowledge: Strong understanding of machine learning algorithms and techniques, including deep learning, supervised and unsupervised learning, reinforcement learning, natural language processing, and generative AI.
Location: Gurugram
Experience: 3+ years
Budget: Upto 35 LPA
Industry: AI Product
Role and Responsibilities:
Model Development: Design, train, test, and deploy machine learning models using frameworks like Pytorch and TensorFlow, specifically for virtual try-on applications with a focus on draping and fabric simulation.
Task-Specific Modeling: Build models for tasks such as Natural Language Processing (NLP), Speech-to-Text (STT), and Text-to-Speech (TTS) that integrate seamlessly with computer vision applications in the virtual try-on domain.
Image Processing: Implement advanced image processing techniques including enhancement, compression, restoration, filtering, and manipulation to improve the accuracy and realism of draping in virtual try-on systems.
Feature Extraction & Segmentation: Apply feature extraction methods, image segmentation techniques, and draping algorithms to create accurate and realistic representations of garments on virtual models.
Machine Learning Pipelines: Develop and maintain ML pipelines for data ingestion, processing, and transformation to support large-scale deployments of virtual try-on solutions.
Deep Learning & Draping: Build and train convolutional neural networks (CNNs) for image recognition, fabric draping, and texture mapping tasks crucial to the virtual try-on experience.
AI Fundamentals: Leverage a deep understanding of AI fundamentals, including machine learning, computer vision, draping algorithms, and generative AI (Gen AI) techniques to drive innovation in virtual try-on technology.
Programming: Proficiently code in Python and work with other programming languages like Java, C++, or R as required.
Cloud Integration: Utilize cloud-based AI platforms such as AWS, Azure, or Google Cloud to deploy and scale virtual try-on solutions, with a focus on real-time processing and rendering.
Data Analysis: Perform data analysis and engineering to optimize the performance and accuracy of AI models, particularly in the context of fabric draping and garment fitting.
Continuous Learning: Stay informed about the latest trends and developments in machine learning, deep learning, computer vision, draping technologies, and generative AI (Gen AI), applying them to virtual try-on projects.
Skills Required:
Experience: Minimum of 5 years in Computer Vision Engineering or a similar role, with a focus on virtual try-on, draping, or related applications.
Programming: Strong programming skills in Python, with extensive experience in Pytorch and TensorFlow.
Draping & Fabric Simulation: Hands-on experience with draping algorithms, fabric simulation, and texture mapping techniques.
Data Handling: Expertise in data pre-processing, feature engineering, and data analysis to support high-quality model development, especially for draping and virtual garment fitting.
Deep Neural Networks & Gen AI: Extensive experience in working with Deep Neural Networks, Generative Adversarial Networks (GANs), Conditional GANs, Transformers, and other generative AI techniques relevant to virtual try-on and draping.
Advanced Techniques: Proficiency with cutting-edge techniques like Stable Diffusion, Latent Diffusion, InPainting, Text-to-Image, Image-to-Image models, and their application in computer vision and virtual try-on technology.
Algorithm Knowledge: Strong understanding of machine learning algorithms and techniques, including deep learning, supervised and unsupervised learning, reinforcement learning, natural language processing, and generative AI.