A highly skilled professional in Electronic Engineering with a strong focus on Computer Vision and Artificial Intelligence, possessing robust qualifications through a Bachelor's and ongoing Master's degree in Electrical Engineering. Demonstrated expertise in image processing, object detection, anomaly classification, and semantic segmentation utilizing advanced frameworks such as PyTorch, TensorFlow, and OpenCV. Current research as an AI & Computer Vision Researcher involves developing software to inspect visual defects in notebooks using sophisticated techniques aligned with Agile methodology. Proficient in handling various communication protocols and embedded systems, combined with practical experience in natural language processing and thermographic imaging solutions. A commitment to innovation is evidenced by participation in international educational programs and ongoing professional development in cutting-edge technologies.

A highly skilled data scientist and computer engineer with a Bachelor’s degree in Computer Engineering from Universidade de Araraquara, a Master’s in Computer Science and Computational Mathematics from the Instituto de Ciências Matemáticas e de Computação at USP, and a recent MBA in Data Science from USP/Esalq. Currently a doctoral candidate, engaged in advanced research focusing on machine learning applications for data quality assessment. Proven ability to apply theoretical knowledge to practical challenges, illustrated by substantial experience at Ford, where innovative facial recognition systems were developed utilizing advanced programming skills in Python and machine learning frameworks. Demonstrates strong analytical capabilities, collaborative spirit, and effective communication skills while mentoring MBA students.

With a robust academic background and extensive post-doctoral experience at the intersection of biology and technology, this candidate currently contributes to innovative projects at Tulane University, USA, utilizing neural networks to enhance the world's largest fish image database for AI applications. Previous tenure at EMBL-EBI in the UK included the development of Python applications that revolutionized biological signal interpretation through machine learning and computer vision for analyzing cardiac rhythms and caudal movements. Experience at the National Institute for Amazonian Research in Brazil established a foundational expertise in scientific research, focusing on advanced technologies for analyzing captive animal behavior. Proficient in technologies such as Python, OpenCV, Scikit-learn, and TensorFlow, combined with a strong command of algorithms and data structures, positions this candidate to adeptly tackle complex challenges in the fields of data science and biology.

Possessing a Bachelor's and a Master's degree in Physics from a state university, this candidate demonstrates expertise in applying mathematical tools, logical reasoning, and scientific methods to practical problem-solving across various domains that utilize modeling and data analysis. The individual showcases substantial proficiency in programming as applied to fields such as Analytics, Machine Learning, and finite element simulations, as well as in control and automation systems. With robust experience in developing and implementing AI, Computer Vision, and advanced machine learning solutions, this candidate has led multidisciplinary teams and adopted MLOps practices in the sector. Furthermore, they are well-versed in documenting projects effectively and delivering impactful oral presentations in both Portuguese and English, making them particularly suited for interdisciplinary collaborations.

With a background in Chemistry from UnB, and currently completing degrees in Big Data and an MBA in Data Science and Analytics, this professional has been actively engaged in artificial intelligence since 2022. Demonstrating expertise in computer vision, natural language processing, and recommendation systems, experience includes hands-on roles as an AI Developer at Apollo Solutions Dev and Cromai, focusing on developing applications with LLMs, ETL processes for both structured and unstructured data, and model training and deployment using advanced techniques in deep learning and transformers. Proficient in Python development, the candidate has successfully designed custom datasets, improved data acquisition pipelines, and presented project outcomes through effective storytelling methodologies, supplemented by an AWS Cloud Practitioner certification.

A Chemical Engineer with a specialization in Pharmacometrics transitioning into Data Science, this candidate possesses advanced skills in clustering, classification, regression, and forecasting using statistical and neural network methodologies. Holding a leadership role as a Data Scientist at PLIN Energy, notable achievements include the development of a forecasting algorithm that significantly improved prediction accuracy while reducing error rates, alongside designing scalable APIs. Previous leadership experiences include serving as Student President for AIChE-Maringá, where strategic decision-making and cultural initiatives were implemented successfully, and as Legal and Financial Director at CONSEQ, where the candidate oversaw substantial financial growth and established the organization as a model within the Junior Companies Movement. This professional foundation is complemented by an education in Chemical Engineering and certifications in Data Science and advanced English proficiency.

Possessing a Master's degree in Applied Artificial Intelligence and a Bachelor's degree in Computer Engineering, this candidate brings extensive experience in corporate project management and data science integration. With expertise in Computer Vision utilizing TensorFlow, Statistical Inference, Big Data Transformation, Deep Learning, Neural Networks, and Image Processing, they have effectively led teams in developing innovative AI software solutions. As a Senior Data Scientist and Project Manager, they have played a pivotal role in constructing artificial intelligence models for energy recovery systems and fraud detection, while embracing each project as a unique challenge. Proficient in methodologies such as Scrum and proficient in software architecture and SaaS solutions, this candidate demonstrates a strong commitment to leveraging computational technologies for impactful business results.

Possessing extensive experience in software engineering, this candidate expertly navigates the software development lifecycle from ideation to deployment and excels in various domains, including API development, Computer Vision, Natural Language Processing (NLP), and AI/ML applications. With a pronounced focus on Data Science, expertise in Python programming, and hands-on experience in utilizing AI/ML techniques on unstructured data types such as video, audio, and text, they are poised to drive impactful data-driven solutions. Their professional journey includes leading the development of applications for data extraction, video analytics, and the implementation of efficient database systems. Committed to generating value through data insights, they demonstrate a strong capacity for collaborating across technical and business teams to deliver refined and functional software solutions.

Apple Core ML is a machine learning framework that enables developers to integrate machine learning models into iOS, macOS, watchOS, and tvOS applications. It allows for on-device processing, providing fast and efficient performance while maintaining user privacy.

An Apple Core ML expert must have skills in Swift and Objective-C programming, experience with Xcode, proficiency in converting and optimizing machine learning models for on-device use, and a solid understanding of Apple's machine learning libraries such as Create ML. Familiarity with model formats like Core ML Model (.mlmodel) is also essential.

How to hire a Apple Core ML expert

Apple Core ML was introduced in 2017 by Apple Inc. It was created to simplify the integration of machine learning models into Apple's ecosystem, allowing developers to run these models directly on devices for improved performance and privacy.

About Apple Core ML

Strengths of Apple Core ML include seamless integration with Apple's ecosystem, on-device processing for enhanced performance and privacy, and support for a variety of machine learning models. Weaknesses include limited cross-platform compatibility and reliance on Apple's hardware. Competitors include TensorFlow Lite, PyTorch Mobile, and 

Apple Core ML

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