7 Mask Rcnn Jobs

As the AI Model Development & Deployment Lead, your primary responsibility will be to design and implement cutting-edge computer vision models for various tasks such as object detection, tracking, segmentation, and action recognition. You should have expertise in architectures like YOLO (v4, v5, v8), Vision Transformers, Mask R-CNN, Faster R-CNN, LSTMs, and Spatio-Temporal Models for in-depth image and video analysis. It will be crucial to contextualize the models for challenging scenarios like poor detection due to occlusion or small, fast-moving objects by training them on diverse situations. Your role will involve identifying reasons for suboptimal model performance and developing a scalable computer vision layer adaptable to different environments. Collaborating with data scientists using frameworks like TensorFlow and Pytorch, you will drive impact across key KPIs and optimize models for real-time inference through techniques such as quantization, pruning, and model distillation. In the realm of Reinforcement Learning & Model Explainability, you will be tasked with developing and integrating reinforcement learning models to enhance decision-making in dynamic AI environments. This will involve working with Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), A3C, SAC, and other RL techniques. Furthermore, you will explore explainability methods such as SHAP, LIME, and Grad-CAM to ensure transparency and interpretability of AI-driven decisions. Additionally, engineering context-aware features from video sequences to extract temporal and spatial insights for improved model explainability and decision accuracy will be part of your responsibilities. Leading the charge in creating a Scalable Data Model for AI Model Training Pipelines, you will oversee the end-to-end data preparation process, including data gathering, annotation, and quality review before training data utilization. The focus will be on enhancing the quality and volume of training data continuously to boost model performance iteratively. Your expertise in planning the data model for future scalability, with the ability to incorporate new data elements in a modular manner, will be essential. Key Technical Skills required for this role include a proven track record in delivering AI products, hands-on experience with advanced AI models, proficiency in managing and mentoring a small team of data scientists, expertise in AI/ML frameworks like TensorFlow, PyTorch, and Keras, effective collaboration with cross-functional teams, and strong problem-solving skills in image and video processing challenges. Nice to have skills include a mindset for experimentation, iterative improvement, and testing to enhance model performance continually, staying updated with the latest AI research, and balancing experimentation with timely product delivery. Collaboration with engineering and product teams to align AI solutions with product goals and deployment of AI models into production are crucial aspects. Additionally, leadership in guiding and mentoring a team of AI scientists, taking ownership of product KPIs related to AI models, and fostering an AI-first culture focused on innovation and continuous improvement will be part of your role.,

We are looking for a Computer Vision Engineer to contribute to the development of AI-driven video analytics solutions tailored for extracting detailed cricket data from various sources such as match footage, live broadcasts, and player tracking systems. In this role, you will be utilizing deep learning techniques, pose estimation algorithms, and image processing methodologies to scrutinize player movements, shot selection, bowling mechanics, and overall match dynamics. Your responsibilities will include: - Developing computer vision models geared towards player tracking, ball trajectory prediction, and shot classification within the realm of cricket analytics. - Extracting real-time insights from video streams, encompassing player positions, footwork analysis, and batting/bowling techniques. - Implementing pose estimation techniques (such as OpenPose, MediaPipe, DeepLabCut) to dissect player body movements. - Employing action recognition models to categorize batting strokes, bowling variations, and fielding plays. You will also be involved in: - Applying various deep learning tools like CNNs, Transformers, YOLO, Mask R-CNN, and Optical Flow for cricket action recognition. - Developing segmentation models to differentiate between players, umpires, and key match elements like the ball, stumps, and pitch zones. - Utilizing GANs and super-resolution techniques to enhance the quality of low-resolution video footage. - Constructing OCR systems for Optical Character Recognition to extract scoreboards and match statistics from live broadcasts. Additional responsibilities include: - Implementing real-time video processing pipelines using OpenCV, FFmpeg, and GStreamer. - Working with multi-angle TV footage and drone feeds to enrich match analysis. - Developing algorithms to ensure low-latency real-time detection of ball speed, swing, and spin. You will also be required to: - Create automated annotation tools for labeling cricket match videos. - Train supervised and unsupervised learning models using cricket datasets. - Collaborate with sports analysts to validate and refine model predictions. Furthermore, your duties will involve: - Deploying computer vision models on edge devices, cloud platforms, and real-time streaming systems. - Optimizing models for high accuracy and low latency in live cricket broadcasts. - Conducting A/B testing and continuous monitoring to enhance model performance. You will also be expected to: - Perform camera calibration for distortion correction and real-world coordinate mapping. - Implement homography transformation and perspective correction for multi-angle alignment. - Apply video trigonometry techniques to calculate player distances, ball speed, angles, and movement trajectories. - Work with multi-camera synchronization and stereo vision for depth estimation in cricket analytics. About the Company: Lifease Solutions LLP believes in the power of design and technology to address challenges and transform ideas into reality. As a prominent provider of software solutions and services, Lifease Solutions is dedicated to delivering innovative and high-quality solutions that drive growth and value for businesses. Headquartered in Noida, India, we specialize in the finance, sports, and capital market sectors, earning the trust of companies worldwide. Our track record showcases our capability to turn minor projects into major successes, and we continuously seek opportunities to help our clients optimize their IT investments.,

The Computer Vision Engineer position in Hyderabad requires someone with 3 to 5 years of experience in developing, implementing, and optimizing deep learning models. As a Deep Learning Engineer, you will be responsible for driving advanced AI solutions across various industries by leveraging your expertise in neural networks, data processing, and model deployment. Your main responsibilities will include owning the product development milestones, ensuring delivery to the architecture, and identifying challenges. You will drive innovation in the product, cater to successful initiatives, and establish engineering best practices for core product development teams within the company. In this role, you will be involved in developing, porting, and optimizing computer vision algorithms and data structures on proprietary cores. You will also engage in research and development efforts focused on advanced product-critical computer vision components, such as feature extraction, tracking objects, and sensor calibration. Solid programming skills in Python and C/C++, as well as experience with TensorFlow, PyTorch, ONNX, MXNet, Caffe, OpenCV, Keras, and various neural networks, frameworks, and platforms are essential. Previous exposure to GPU computing, HPC, cloud services like AWS/Azure/Google, and NoSQL databases will be beneficial. You should have hands-on experience in deploying efficient Computer Vision products, implementing research papers, using dockerized containers with microservices, and optimizing models for TensorRT. Familiarity with NVIDIA Jetson Nano, TX1, TX2, Xavier NX, AGX Xavier, Raspberry Pi, and edge devices is required. Understanding of computer vision concepts like photogrammetry, multi-view geometry, visual SLAM, detection and recognition, and 3D reconstruction is crucial. You will need to write maintainable, reusable code, leverage test-driven principles, and develop high-quality computer vision and machine learning modules. Experience with object detection, tracking, classification, recognition, scene understanding, and deep neural networks is important. Furthermore, knowledge of image classification, object detection, and semantic segmentation using deep learning algorithms is desirable. You should be able to evaluate and advise on new technologies, vendors, products, and competitors. Initiative, independence, teamwork, and hands-on technical expertise are key attributes for this role. If you are ready to contribute to cutting-edge AI solutions and drive innovation in computer vision, apply now and join our awesome squad.,

As an AI Computer Vision Engineer with 4+ years of experience, you will be responsible for developing and training computer vision models for various tasks such as object detection, image classification, and face recognition. You will optimize models for performance on edge devices and apply data augmentation techniques to image/video datasets. Additionally, you will need proficiency in Large Language Models and a strong understanding of statistical analysis and machine learning algorithms. Your role will involve hands-on implementation of machine learning algorithms like linear regression, logistic regression, decision trees, and clustering algorithms. Understanding image processing concepts and experience in model optimization, quantization, or deploying to edge devices will be essential. You should have strong programming skills in Python (or C++) and expertise in implementing and optimizing machine learning pipelines for seamless integration into production systems. Experience with real-time computer vision applications, OpenCV, NumPy, PyTorch/TensorFlow, and computer vision models like YOLOv5, Mask R-CNN will be beneficial. Engaging with multiple teams, contributing to key decisions, and providing solutions that apply across multiple teams are expected from you. You will lead the implementation of large language models in AI applications and research cutting-edge AI techniques to enhance system performance. Contribution to the development and deployment of AI solutions across various domains is also part of your responsibilities. In terms of requirements, you should design, develop, and deploy ML models for OCR-based text extraction, table and line-item detection, and named entity recognition. Evaluating and integrating third-party OCR tools, developing pre-processing and post-processing pipelines for image/text data, and familiarity with video analytics platforms are necessary skills. Experience with MLOps tools, academic CV research, and knowledge of GPU acceleration or hardware integration will be advantageous for this role.,

As a Lead Data Scientist/AI Product Owner at a reputed IT MNC in Gurgaon, your primary responsibility will be AI Model Development & Deployment, focusing on Computer Vision & Deep Learning. You will be leading the design and implementation of computer vision models for various tasks such as object detection, tracking, segmentation, and action recognition. Your expertise will include architectures like YOLO (v4, v5, v8), Vision Transformers, Mask R-CNN, Faster R-CNN, LSTMs, and Spatio-Temporal Models for image and video analysis. Additionally, you will contextualize the model for challenging situations such as poor detection through specific training on different scenarios. Another key aspect of your role will involve Reinforcement Learning & Model Explainability, where you will develop and integrate reinforcement learning models to optimize decision-making in dynamic AI environments. You will work with techniques such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), A3C, SAC, and other RL methods. Furthermore, you will lead the development of a Scalable Data Model for AI Model Training Pipelines, overseeing the end-to-end data preparation process, including data gathering, annotation, and quality review before training. You will focus on enhancing the quality and volume of training data to continually improve model performance and have the ability to think through the data model for future enhancements. In terms of technical skills, you are expected to have a proven track record of leading and delivering AI products, hands-on expertise with advanced AI models, experience in managing and mentoring a small team of data scientists, proficiency in AI/ML frameworks like TensorFlow, PyTorch, and Keras, effective collaboration with cross-functional teams, and strong problem-solving skills in image and video processing. Nice-to-have skills include Experimentation, Iterative Improvement and Testing, Collaboration with Engineering and Product Teams, and Team Leadership. To qualify for this role, you should have at least 10 years of hands-on experience in AI with a solid background in object detection, image and video processing, and computer vision.,

As a Machine Learning Engineer specializing in Computer Vision, you will play a crucial role in designing, developing, and deploying cutting-edge computer vision models and algorithms for real-world robotic applications. Working alongside a talented team, you will utilize state-of-the-art deep learning techniques to solve complex vision problems ranging from object detection to image segmentation and 3D perception. Your contributions will directly influence the evolution of intelligent systems within the field of robotics. This position is based on-site in Pune and requires immediate availability for joining within one month. Qualifications: - Educational Background: B.Tech., B.S., or B.E. in CSE, EE, ECE, ME, Data Science, AI, or related fields with a minimum of 2 years of hands-on experience in Computer Vision and Machine Learning. Alternatively, an M.S. or M.Tech. in the same disciplines with 2 years of practical experience in Computer Vision and Python programming. - Technical Expertise: You should possess strong experience in developing deep learning models and algorithms for various computer vision tasks, including object detection, image classification, segmentation, and keypoint detection. Proficiency in Python and ML frameworks like PyTorch, TensorFlow, or Keras is essential. Additionally, experience with OpenCV for image processing and computer vision pipelines is required. A solid understanding of convolutional neural networks (CNNs) and other vision-specific architectures such as YOLO, Mask R-CNN, and EfficientNet is expected. Ability to build, test, and deploy robust models using PyTest or PyUnit testing frameworks is necessary. Hands-on experience with data augmentation, transformation, and preprocessing techniques for visual data is also a key requirement. Familiarity with version control using Git is a plus. - Desirable Skills: Experience with 3D vision, stereo vision, or depth sensing technologies will be advantageous. Familiarity with ROS2 for integrating vision systems into robotic platforms, understanding of sensor fusion techniques (e.g., LiDAR, depth cameras, IMUs), exposure to MLOps for deploying and maintaining computer vision models in production environments, knowledge of CMake for building and integrating machine learning and vision-based solutions, experience with cloud-based solutions for computer vision, and ability to work with CUDA, GPU-accelerated libraries, and distributed computing environments are desirable skills. Location: This role is on-site in Pune, India, and immediate availability for joining is required. Why Join Us - Shape the future of robotics and AI by focusing on state-of-the-art computer vision applications. - Work in a dynamic and collaborative environment with a team of highly motivated engineers. - Competitive salary and benefits package, including performance-based incentives. - Opportunities for growth and professional development, including mentorship from industry experts. If you are passionate about leveraging deep learning and computer vision to develop intelligent systems that perceive and interact with the world, we look forward to hearing from you!,

Ability to multitask and work on multiple engagements related to different domains Work in a highly collaborative and fast paced environment by interacting with the stakeholders and various IT teams within the company to facilitate the design and development of ML/AI solutions Responsible for the successful delivery of all allocated projects with respect to schedule, quality, and customer satisfaction Work with the pre-sales team on RFP, RFIs and help them solutioning for different AI/ML use cases Evaluate latest technologies, decide technical feasibility, and drive solution implementations Follow Agile standards and methodologies in all phases of the project and ensure excellence in delivery to customers Refine coding standards, software development guidelines, and best practices within the organization, and ensure adherence to those Mentor other associate architects, engineers and young talent within the organization, define and track their growth parameters. Must have skills: Strong interpersonal and written skills with clear and precise communication. Experience working in an Agile and competitive environment. Technical leadership experience handling large teams. Stakeholder interaction experience both within the organization and outside with clients. Strong analytical and quantitative skill set with proven experience solving business problems across domains. Very good with EDA, Hypothesis Testing, Feature Engineering. Hands-on with Python/R programming and ML/Viz. libraries/frameworks like Scikit-Learn, Pandas, Matplotlib, Seaborn, D3.js, Tensorflow, Pytorch, Keras. Experience with ML algorithms such as Regression and Classification (Decision-trees, Random Forests, SVM, ANNs), Clustering (k-means, DBSCAN), Dimension Reduction (PCA, SVD), Ensemble techniques (XGBoost, CatBoost, LightGBM). Basic image enhancement techniques such contrast enhancement, blurring, histogram equalization, etc using OpenCV. Experience with DL/CV techniques like CNNs, Faster RCNN, Mask RCNN, YOLO, SSD, Detectron2 for various use cases related to image such as Image Classification, Object Detection, Image Segmentation, etc. Traditional NLP - Bag of words, tf-idf, Stemming, Lemmatization, Tokenization, POS tagging, Coreference Resolution, Dependency and Constituency Parsing, Named Entity Recognition. NLP: NLU vs NLG, Vector Space modeling and text representation techniques in NLP, Knowledge/experience using RNNs, LSTMS, Sequence modeling and Attention mechanism, Transformers, BERT, GPT and their SOTA variants, Sequence modeling, Attention modeling, BERT, Transformers.Using the above-mentioned techniques for Text classification, Sentiment Analysis, Semantic similarity, Entity Extraction, Document summarization, NLI, Question-Answering, Machine Translation, etc. Forecasting modeling experience both on univariate and multivariate data using algorithms like Linear Regression, Neural Networks, Exponential Smoothing, Holt s Winters, ARIMA, SARIMA, LSTM. Identify appropriate objective functions, regularization techniques, performance metrics based on the use case and should be able to perform cross-validation, hyperparameter tuning, and error analysis. Experience/Knowledge working on Intelligent Document processing solutions using open-source technologies like OCR using Tesseract, text-block, ROI detection using OpenCV, etc. Experience/Knowledge working on solutions using Cloud based Document processing products like Google Document AI, Amazon Textract, Microsoft Azure Form Recognizer, etc. Proven experience building and deploying AI/ML solutions in production using open source or cloud tools such as MLflow, Kubeflow, TFX, Feature Store, etc. Hands-on AI/ML experience with any one cloud platform (GCP, Azure, AWS) either using the modeling options (Vertex AI, SageMaker, Azure ML) or leveraging the APIs (Textract,Vision, Text Extraction, Speech-to-text, Text-to-speech, Translation etc.). Nice to have skills: Any pre-sales experience Hands-on experience with any visualization tool like Tableau, Power BI, Looker Studio Build APIs using frameworks such as Flask, Django, FastAPI Distributed training for deep learning using frameworks like PyTorch, TensorFlow Advanced image processing using OpenCV, Feature Detection and Matching using SIRF/SURF/FAST/BRIEF Advanced recommender systems using model based techniques like KNN, Matrix Factorization, SVD, etc and and/or Deep Learning methods Docker containerization of microservices, deployment on cloud compute resources and orchestration using popular frameworks like Kubernetes Experience with either or all of Knowledge Graphs, Federated Learning, Deep Reinforcement Learning Cloud Certification - Machine Learning and/or Cloud Architect