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.

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.

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.

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.

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 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 postgraduate degree in Machine Learning and an undergraduate degree in Spanish Language Studies, this candidate aims to further their expertise in Natural Language Processing (NLP). They bring over a decade of experience as a language educator and have been actively engaged as a translator and interpreter since 2018, expressing a keen interest in expanding professional experience in the IT sector. With over two years of hands-on experience in Python and data preprocessing libraries for predictive models, including classical algorithms and Transformers, they hold a Professional Certificate as a TensorFlow Developer from DeepLearning.AI. Additionally, they have authored a published paper on models applied to the classification of Spanish dialects and are fluent in Portuguese and Spanish, with advanced proficiency in English.

KubeFlow Pipelines is a platform for building, deploying, and managing end-to-end machine learning workflows on Kubernetes. It enables the orchestration of complex ML tasks, versioning of pipelines, and easy integration with other tools in the ML ecosystem.

A KubeFlow Pipelines expert must have strong skills in Kubernetes, Docker, and YAML for configuration management. Proficiency in Python is essential for developing pipeline components. Knowledge of machine learning frameworks like TensorFlow or PyTorch is crucial. Familiarity with CI/CD tools and experience with cloud platforms such as GCP, AWS, or Azure are also important.

How to hire a KubeFlow Pipelines expert

KubeFlow Pipelines was developed as part of the KubeFlow project, which originated at Google in 2018. It was created to simplify the process of deploying and managing machine learning workflows on Kubernetes, aiming to address the complexity of orchestrating ML tasks and integrating various tools in the ecosystem.

About KubeFlow Pipelines

Strengths of KubeFlow Pipelines include its ability to orchestrate complex ML workflows, seamless integration with Kubernetes, and strong support for versioning and reproducibility. Weaknesses involve a steep learning curve and potential complexity in setup and maintenance. Competitors include Apache Airflow, MLflow, and TFX (TensorFlow Extended).

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How to hire a KubeFlow Pipelines expert

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