Leonardo dos Santos Pinheiro, Developer in Brisbane, Queensland, Australia
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Leonardo dos Santos Pinheiro

Verified Expert  in Engineering

Machine Learning Engineer and Software Developer

Brisbane, Queensland, Australia
Toptal Member Since
July 4, 2019

Leonardo is a machine learning engineer with 10 years of industry experience across the government, energy markets, finance, healthcare, and consulting. Leonardo is well versed in work with analytics, data engineering, and machine learning, specializing in the development and deployment of AI systems for computer vision, NLP, and recommender systems.


TypeScript, Azure, PyTorch, ChatGPT, Natural Language Processing (NLP)...
Java, OpenAI, AWS Serverless Application Model (AWS SAM), AWS Lambda, JavaScript
Kwan Ting Chang
Artificial Intelligence (AI), Natural Language Processing (NLP), Speech to Text...




Preferred Environment

Visual Studio Code (VS Code), Jupyter, Linux, PyCharm, Windows Subsystem for Linux (WSL)

The most amazing...

...thing I've done was to develop end-to-end machine learning pipelines for cancer detection—from data selection for labeling to deployment on Kubernetes.

Work Experience

Senior ML Engineer

2023 - PRESENT
  • Created video editing functionality, including intent detection, commanding, Q&A, and summarization, using LangChain and ChatGPT with video transcripts.
  • Integrated audio analysis models for noise canceling, audio classification, and voice activity detection in a video editor. Deployed models on the client application using ONNX Runtime and in Azure back end.
  • Integrated the research img2vid model based on Stable Diffusion and ControlNet into the video editor. Worked on model profiling and resource optimization.
Technologies: TypeScript, Azure, PyTorch, ChatGPT, Natural Language Processing (NLP), Computer Vision

AI-first Software Engineer

2024 - 2024
  • Developed chat and summarization functionality for the Jive CoPilot chatbot using OpenAI API.
  • Tracked and fixed copilot-related bugs on both Jive Cloud and Jive Hop.
  • Worked on the specification of AI pipelines to automate software engineering tasks such as test generation, documentation generation, and PR description.
Technologies: Java, OpenAI, AWS Serverless Application Model (AWS SAM), AWS Lambda, JavaScript

NLP Expert via Toptal

2024 - 2024
Kwan Ting Chang
  • Worked with the client to create a dataset for TTS. Helped with script generation, voice actor hiring, data distribution analysis, speech-and-text emotion analysis, and data preprocessing.
  • Fine-tuned StyleTTS 2 model for text-to-speech with emphasis on capturing the diversity of emotional tones.
  • Delivered a full TTS system for integration with a larger web application based on Python, PyTorch, FastAPI, and Docker.
Technologies: Artificial Intelligence (AI), Natural Language Processing (NLP), Speech to Text, StyleTTS, Tortoise TTS, vosk-tts, Tacotron 2

Senior ML Engineer

2021 - 2023
  • Handled the end-to-end pipeline for cancer detection, including labeling with V7, model building for semantic segmentation using Hugging Face, and deployment on Kubernetes using FastAPI.
  • Contributed to an evaluation system for the CT brain classification model. Built internal Python library for multiple hypothesis statistical testing.
  • Led a series of transformer-based experiments for embryo selection to improve the performance of a production system based on Inception3D. The ViViT experiments led to a better model, which was later moved to production.
  • Built a chat application based on semantic search using an LLM to aid clinicians in finding medical reports containing specific cases of interest. The application was used to perform case selection for image labeling.
  • Contributed to a chatbot for retrieving medical cases based on semantic search using OpenAI ChatGPT and LangChain.
Technologies: TensorFlow, PyTorch, Amazon Web Services (AWS), Docker, Artificial Intelligence (AI), Machine Learning, Chatbots

Senior ML Engineer

2021 - 2023
Jungle Scout
  • Developed an MLOps system for automatic model evaluation and promotion for an eCommerce weekly model training pipeline using SageMaker, MLflow, Kedro, and Airflow.
  • Productized deep learning models for time series eCommerce models based on PyTorch Lightning and SageMaker.
  • Created a new model serving pipeline based on the MLflow model registry and SageMaker endpoints, with Lambda and an API gateway for scaling and Datadog monitoring.
  • Created a model performance dashboard based on Plotly Dash and Redis. Deployed on Fargate.
Technologies: Amazon Web Services (AWS), TensorFlow, Docker, Deep Learning, Python, Machine Learning, Time Series

Senior Software Engineer (Data and AI)

2019 - 2021
  • Used stereo vision and image segmentation on satellite imagery to aid an infrastructure company in vegetation management. The system was used to map the risk of vegetation encroachment with assets.
  • Developed a Twitter analysis dashboard to measure tweet sentiments, a network of influencers, and visualize trends per tag/time to aid strategic designers in research.
  • Developed a gradient-boosting model for activity classification using sensor data for a supply chain startup. The system was used to track illegal activity at different points in the supply chain.
  • Built image classification models for crop recognition and crop pest/disease recognition for a farming startup. The system supported advisory for smallholder farmers in Southeast Asia.
  • Built a recommender system for a cashback program startup, enabling personalization of content to drive engagement in the platform.
  • Created a performance dashboard for a farming startup using Data Studio and BigQuery.
Technologies: Amazon Web Services (AWS), Docker, TensorFlow, OpenCV, Python, Machine Learning, JavaScript, Node.js, Azure

Senior Machine Learning Consultant

2018 - 2019
  • Developed and deployed a churn model using gradient boosting for an insurance company.
  • Developed and deployed a convolutional network for customer spending forecasting using TensorFlow, Ansible, Docker, ECS, DynamoDB, and PostgreSQL.
  • Developed and deployed a text classification system using a convolutional model using TensorFlow and Spark.
  • Designed a data science strategy for a major financial institution. Mentored junior data scientists.
  • Explored a large corpus of insurance claims data using association rule mining, topic modeling, semantic similarity, and other text mining techniques.
  • Created a open domain chatbot based on machine comprehension (Facebook's DrQA) using PyTorch, Flask, React, and DialogFlow.
  • Assisted in the development of a person tracking system using Yolo v2 and Kalman filters for a major Australian retail company.
  • Assisted with a markdown system based on demand forecasting using Facebook's Prophet and revenue optimization using mixed-integer linear programming.
Technologies: Amazon Web Services (AWS), Keras, TensorFlow, Hadoop, Spark, Scala, Python, Agile Data Science, Machine Learning, Chatbots

Data Scientist

2017 - 2018
Mojo Power
  • Developed and deployed a serverless linear model for load forecasting using Python, NumPy, and AWS Lambda.
  • Created a proof-of-concept Hidden Markov Model for load disaggregation.
  • Developed a model for credit scoring of energy customers.
  • Developed and deployed an LSTM model for load forecasting using PyTorch.
  • Developed dashboards for analytics reporting on energy usage using Tableau.
  • Used topic modeling for exploratory data analysis of customer reviews.
  • Worked on a PoC for solar panel detection on satellite images using Facebook's Detectron.
Technologies: Tableau, PostgreSQL, AWS Lambda, Python, Data Science, Machine Learning

Quantitative Developer

2016 - 2017
Macquarie Bank
  • Parsed and analyzed unstructured data of logs of order execution into SQL Server.
  • Back-tested optimal execution strategies.
  • Developed a Plotly dashboard to visualize market data.
  • Tested and investigated new trading strategies.
  • Tested machine learning algorithms for commodities trading.
Technologies: Plotly, Django, Vagrant, Microsoft SQL Server, Kdb+, Python

Quantitative Researcher

2012 - 2016
Comissão de Valores Mobiliários
  • Developed regulatory research studies using statistical modeling (estimation and hypothesis testing).
  • Created market risk reports and visualizations with time series analysis and forecasting using R.
  • Elaborated a risk monitoring system using Monte Carlo simulation and statistical estimation using Java.
  • Developed a data warehouse to aggregate data related to market risk and development of BI reports using BusinessObjects.
  • Led a data governance group to discover and catalog data sources across the whole organization.
Technologies: Microsoft 365, SAP BusinessObjects (BO), Python, SPSS, Microsoft SQL Server, R, SQL

Business Analyst

2010 - 2012
Brazilian Institute of Metrology
  • Worked with operational teams to develop scripts to collect metrics from operational processes and automate reporting. Scripts were based on Python and PowerShell.
  • Built Cognos dashboards to monitor business KPIs related to operational processes. Also made custom Jupyter Notebooks for additional data analysis.
  • Created a simplified database/warehouse using SQLite to aggregate data from multiple spreadsheets and support live business dashboards.
Technologies: Dashboards, Python, Windows PowerShell, SQL

Investment Funds Program

A program that creates a network of interconnections between investment funds and simulates a cascading failures algorithm. The program serves as a stress testing model that needs data to run and can be executed from the command line by typing "python fin_contagion_inv_funds.py."
2014 - 2016

Master's Degree in Applied Math

Getulio Vargas Foundation - Rio de Janeiro, Brazil

2006 - 2009

Bachelor's Degree in Management Science

Getulio Vargas Foundation - Rio de Janeiro, Brazil


AWS Certified Developer



Keras, Scikit-learn, XGBoost, Pandas, NumPy, SpaCy, OpenCV, TensorFlow, SciPy, Natural Language Toolkit (NLTK), Luigi, PyTorch, Flask-RESTful, Node.js, Tortoise TTS


Jupyter, Tableau, Plotly, H2O AutoML, GitLab CI/CD, Git, Amazon Elastic Container Service (Amazon ECS), Apache Airflow, IntelliJ IDEA, SPSS, Vagrant, AWS CloudFormation, Talend ETL, PyCharm, MATLAB, ChatGPT


Spark, Hadoop, Windows PowerShell, Django, Flask, AWS Serverless Application Model (AWS SAM)


Python, SQL, R, Scala, Bash, Julia, JavaScript, TypeScript, Java


Data Science, Scrum, Kanban


Amazon S3 (AWS S3), Amazon DynamoDB, InfluxDB, PostgreSQL, Microsoft SQL Server, MongoDB, Kdb+, Neo4j


Docker, AWS Lambda, Linux, Google Cloud Platform (GCP), Amazon Web Services (AWS), Apache Kafka, Visual Studio Code (VS Code), Amazon EC2, Azure


Artificial Intelligence (AI), Dashboards, Agile Data Science, Machine Learning, Natural Language Processing (NLP), Computer Vision, Deep Learning, Generative Pre-trained Transformers (GPT), A/B Testing, Visualization, Statistics, APIs, Scraping, Analytics, Dashboard Design, Chatbots, SAP BusinessObjects (BO), Microsoft 365, Recommendation Systems, Windows Subsystem for Linux (WSL), Amazon RDS, ECS, Time Series, Optimization, Graphs, Speech to Text, StyleTTS, vosk-tts, Tacotron 2, OpenAI

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