Fernando Camargo

WORK HISTORY

EDUCATION

SKILLS

ABOUT FERNANDO CAMARGO

Hey there! I\'m Fernando Camargo, a Machine Learning Engineer and Researcher based in Vancouver, Canada, with over 13 years of experience in software engineering. My passion lies in crafting and optimizing machine learning systems to create impactful data products that drive business growth.At Klue, I led the development of sophisticated models, such as a hierarchical event tagging system. This improved content curation and customer satisfaction by tagging over 400k news articles daily.In previous roles, I contributed to pioneering research in Recommender Systems, publishing papers and creating open-source tools that gained over 20 citations. In healthcare, I developed a model for identifying pulmonary anomalies in chest X-rays, potentially saving annually and reducing patient wait times by up to 5 hours.With a Ph.D. in Computer Science and publications in journals like Nature Computational Science, I\'ve also taught Deep Learning to over 80 students and mentored interns, supporting their growth.My strength lies in delivering end-to-end machine learning projects, from research, data collection, and modelling to deployment and monitoring. With a background in Software Engineering, I prioritize writing efficient, production-ready code.As an AI Mentor and Machine Learning Strategist, I partner with forward-thinking companies to unlock their full potential through advanced AI solutions. By aligning our goals and investing in your success, I ensure that our collaboration drives tangible, scalable growth, making AI a cornerstone of your competitive advantage.Feel free to reach out at f••••••••@gmail.com.Competencies: Python, SQL, Apache Spark, BentoML, Docker, ElasticSearch, Git, Google Cloud Platform (GCP), Kubernetes, Linux, MongoDB, PostgreSQL, Redis, FastAPI, Jupyter Notebook, Keras, Luigi, Matplotlib, NLTK, NumPy, OpenCV, Pandas, PyTorch, SciPy, Scikit-Learn, TensorFlow, A/B Testing, Algorithm Design, Classification, Clustering, Convolutional Neural Network, Data Analysis, Data Science, Data Structures, Decision Tree, Extract Transform Load (ETL), Few-Shot Learning, Fine Tuning, GenAI, Gradient Boosting, Hyperparameter Optimization, Linear/Logistic Regression, MLOps, Natural Language Processing (NLP), Object Detection, REST API, Random Forest, Recommendation System, Recurrent Neural Network, Regression, Retrieval Augmented Generation, Segmentation, Statistical Analysis, System Design, Time Series Analysis, Topic Modeling, Transformer