About me

machine learning engineer

Rodrigo Gonzalez is an experienced Machine Learning and Data Science Architect with over a decade of success in developing advanced AI solutions across various industries. He possesses deep expertise in natural language processing, computer vision, deep learning, and multi-modal foundation models. Rodrigo has a strong track record of transforming experimental models into real-world, market-ready products, focusing on integrating complex algorithms into AutoML and Generative AI solutions. He specializes in building scalable implementations that drive business growth and is skilled in cloud platforms and big data technologies.

With a robust foundation in mathematical modeling, statistics, and advanced machine learning techniques, Rodrigo excels at the intersection of data and strategic business planning. His creative application of data analytics and machine learning, combined with a strong background in finance and consulting, empowers executives to make informed, strategic decisions. His work encompasses detecting patterns, building models, and extracting insights from large datasets, underpinning a full-cycle, data-driven approach to product development and business improvement.

work experience

As the Founder and Machine Learning Engineer at AI Consulting, Rodrigo collaborates directly with clients to identify AI and ML use cases and opportunities. By developing tailored AI solutions using frameworks like TensorFlow, PyTorch, and Scikit-Learn, he enhances operational efficiency, informs strategic decision-making, and unlocks new avenues for growth and profitability. His hands-on approach ensures that the AI solutions are seamlessly integrated with client operations, utilizing advanced cloud platforms to automate workflows and analytics.

Previously, Rodrigo served as a Senior Machine Learning Engineer at DataRobot, where he led significant projects such as developing daily time-series predictive models for COVID-19 cases and deaths. This work expedited vaccine trials and accelerated the delivery of crucial treatments. He also integrated advanced model algorithms into AutoML products, optimized preprocessing, and substantially boosted model performance.

Earlier in his career, as a Data Scientist at UTC Aerospace Systems (UTAS), Rodrigo architected and deployed deep learning models for object recognition and detection in aerial imagery, significantly boosting accuracy. He also designed AI systems for image labeling and signal processing software, enhancing national security and operational efficiency.

Rodrigo's rich portfolio of projects across various domains demonstrates his capability to apply cutting-edge machine learning and data science techniques to solve real-world challenges. Whether automating workflows, refining business strategies, or enhancing object detection accuracy, his contributions have consistently delivered tangible improvements and innovation.

Driven by a deep interest in data and innovation, Rodrigo aims to use his expertise to develop impactful products and support team growth. He is effective in leading and collaborating with teams, communicating technical concepts clearly, and applying a meticulous, analytical approach to problem-solving.

technical skills

• Programming Languages: Proficient in Python, Scala, R, Bash, and SQL with experience in Golang, Lua, Typescript, Java, JavaScript, and node.js.

• Machine Learning Frameworks & Libraries: Expertise in Pandas, NumPy, SciPy, PySpark, PyTest, Flask, PyTorch, TensorFlow, TensorBoard, Keras, Scikit-Learn, HuggingFace, WandB, XGBoost, LightGBM, CatBoost, spaCy, Hyperopt, LangChain, SentenceTransformers. Experience with Django, Apache Spark, Kafka, Kubernetes, CUDA, TensorRT, Intel HLS, LAPACK, BLAS, OpenMP, Open MPI, and LaTeX.

• Data Science Skills: Proficient in Econometrics, Predictive Analysis, Quantitative Analysis, EDA, ETL, Feature Engineering, Cross-Validation & Model Evaluation, Data Modeling, Explanatory Analysis, Explainable AI, Time Series Analysis, Genetic Algorithms, and Signal Processing.

• Machine Learning Techniques: Skilled in Frequentist & Bayesian Statistics, Regression & Classification, Regularization, Ensemble Methods, Neural Networks, Hidden Markov Models (HMMs), Anomaly Detection, Unsupervised Learning, Semi-Supervised Learning, Boosting, Natural Language Processing, Expertise in Generative NLP Models including BERT & Transformer models, Embedding Techniques, Language Representation Models, Image Recognition & Computer Vision, Computational Signal and Audio Processing, Reinforcement Learning, Transfer Learning, and Bayesian Hyperparameter Optimization.

• DevOps, MLOps & Development Tools: Proficient in PyCharm, Docker, Kubernetes, Jenkins, Luigi, MLflow, JIRA, GitHub, and Git with a strong understanding of MLOps principles for efficient deployment, monitoring, and maintenance of machine learning models.

• Cloud Platforms: Skilled in AWS and GCP.

• Databases: Proficient in MongoDB, BigQuery, Athena, Postgres, SQL, DynamoDB, Cassandra, Redis, SQLServer, Amazon Redshift.

• Data Visualization Tools: Skilled in Matplotlib and Seaborn with experience in D3.js, Gephi, and Tableau.

• Operating Systems: Proficient in Linux Ubuntu and macOS with experience in RHEL and Linux CentOS.

academic background

Rodrigo received a BA in Quantitative Economics and a BS in Chemical & Biological Engineering from the University of Colorado at Boulder in 2012, graduating magna cum laude. In 2015, he earned an MA in Finance from Harvard University. During his studies at Harvard, he founded Bloom Consulting (now AI Consulting LLC), where he spearheaded the creation of credit analysis algorithms using machine learning for small business lending platforms. He also managed technology and financial product development, economic and financial analysis, and the firm's strategic direction.

His academic pursuits include original tissue engineering, quantum mechanics, and econometrics research, encompassing investigations into biocompatible PEG hydrogels, computational quantum chemical analysis, and the cyclical characteristics of firm leverage through vector autoregression (VAR) modeling.

interests

- algorithms & data structures
- large scale software architecture
- mathematical programming
- combinatorics
- optimization