Pietro is available for hire
Hire PietroPietro Grandinetti
Verified Expert in Engineering
Data Science Developer
Milan, Metropolitan City of Milan, Italy
Toptal member since November 20, 2017
Bio
Pietro is a systems engineer with a professional background as a software developer and a PhD in large-scale network optimization. Equally at ease in applied data science and software positions, he enjoys designing and developing systems in Python, Go, C, and R, with AWS and GCP. In recent years, Pietro has held C-level roles (CTO/President), while also keeping his engineering skills sharp.
Portfolio
Pentadata
Amazon Web Services (AWS), Python, React, PostgreSQL, AWS Lambda, Flask...
Toptal Client
Amazon Web Services (AWS), APIs, React, REST, PostgreSQL, Docker, Python, Go...
Motorola Solutions
REST, SQL, Python, Amazon Web Services (AWS)
Experience
- Data Science - 10 years
- PostgreSQL - 10 years
- Python - 10 years
- APIs - 7 years
- Go - 7 years
- Amazon Web Services (AWS) - 6 years
- Docker - 6 years
- Haskell - 3 years
Work Experience
Chief Technology Officer
2020 - PRESENT
Pentadata
- Built back-end systems that comprised several microservices, including APIs, Cron, AWS RDS, AWS Lambda, Elasticsearch, and Docker Swarm. Used Terraform to develop and maintain reproducible infrastructures.
- Managed a team of software developers and contractors.
- Passed a SOC 2, Type 2 official security audit, with final reporting about the company's procedures, controls, data protection, and privacy. Interacted with the CISO of financial institutions to review controls, procedures, and compliance.
- Integrated multiple financial data sources into a new and unique API system with data cleaning and aggregation.
- Passed the penetration test on the AWS infrastructure, including servers, database, and file storage.
- Developed machine learning algorithms and deployed them into the production environment, serving more than a million daily requests.
- Created GRPC services in Go to implement authentication, storage lookup, and RPC integration with other programming languages.
Technologies: Amazon Web Services (AWS), Python, React, PostgreSQL, AWS Lambda, Flask, Big Data, Machine Learning, Amazon DynamoDB, Amazon RDS, FastAPI, Docker Swarm, Docker, Linux, Site Reliability Engineering (SRE), Elasticsearch, Go, Terraform, Fintech, SOC 2, Vanta
System Architect | Developer
2020 - 2020
Toptal Client
- Built the MVP of an API platform in Python/Flask and deployed scalable architecture in AWS and RDS.
- Designed a machine learning API built off scikit-learn and deployed in Flask.
- Designed highly scalable architecture in AWS and passed penetration testing and security audits.
Technologies: Amazon Web Services (AWS), APIs, React, REST, PostgreSQL, Docker, Python, Go, Terraform
Software Developer
2018 - 2020
Motorola Solutions
- Served as a core member of the Python development team.
- Constructed a data engineering pipeline, which included Python, SQL, and Oracle Cloud Commerce.
- Built a batch processing infrastructure for periodic big data engineering.
- Interfaced Python code with different SQL database systems.
- Created REST APIs and triggers for cron jobs, deployed in the AWS Linux server.
Technologies: REST, SQL, Python, Amazon Web Services (AWS)
Research Scientist
2018 - 2018
UCLA
- Participated in the Long Program: Science at Extreme Scales—Where Big Data Meets Large-scale Computing.
- Contributed to state-of-the-art research on big data and machine learning.
- Defined new directions of industrial and academic research directions in the field.
Technologies: Large-scale Computing, Big Data
Python Developer
2017 - 2018
Gridcell (via Toptal)
- Developed a RESTful API in a Django REST Framework.
- Created a WebSocket connection with Django channels.
- Built the client- and server-side for data streaming through AWS IoT and AWS Kinesis.
- Developed and queried Elasticsearch indexes via a Python client.
Technologies: Elasticsearch, Amazon Web Services (AWS), Django Channels, Django, Python
Data Scientist
2017 - 2018
RankSense
- Implemented convex and non-convex optimization algorithms for the reranking of web pages.
- Implemented forecast and prediction of page sales time series.
- Developed an exploratory analysis of Google Analytics Data.
- Created large-scale optimal procedures in native scientific Python, with NumPy, and Pandas.
- Developed deep learning and artificial intelligence (AI) systems for automated search engine optimization (SEO).
Technologies: Deep Learning, Python, Search Engine Optimization (SEO)
Software Developer
2017 - 2018
LegalKite
- Built a REST API for LegalKite.com with a Django-REST framework.
- Scraped Swiss law code and stored the data using PostgreSQL which allowed the creation of personal annotations.
- Supported search optimization with Elasticsearch through a database of laws and annotations.
- Deployed the system entirely into AWS facilities (RDS, Elasticsearch service, and EC2).
- Wrote deep learning and latent semantic indexing (LSI) algorithms for text classification.
Technologies: Amazon Web Services (AWS), PostgreSQL, Elasticsearch, Django
Software Developer
2017 - 2017
Bluesophy
- Created a chatbot that acts as recommendation system.
- Incorporated smart search methods in the existing database with a Django ORM.
- Installed a syntactic analysis of input sentences, using NLTK.
- Integrated the bot within a standard web architecture (Angular and Django).
- Generated human-friendly answers with a hyperlink redirection to favorite items.
Technologies: Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Django, Python
Research Engineer
2014 - 2017
CNRS
- Created a microscopic simulator of a road traffic network in Grenoble (FR), using the Aimsun simulator.
- Designed a centralized and distributed control algorithms for intelligent traffic lights.
- Integrated numerical optimization procedures into the Aimsun architecture.
- Created simulative comparisons with an industrial-strength controller.
- Developed a big-data analysis of microscopically generated road traffic data.
Technologies: Aimsun, MATLAB, Python
Project History
Portability | The Future Of Financial Data
In this article, published on the Forbes' Technology Council, I discuss data portability as it relates to the fintech industry. Data portability affirms that the consumers own the data and have the right to port it with themselves onto any platform of their choosing.
Put Data Ownership Where It Belongs: With The Consumer
This is an article of mines that the Forbes Technology Council published. The article deals with themes related to online privacy and consumer control of financial data. I wrote it while representing Pentadata, Inc, as its CTO.
Road Network Macrosimulator
https://github.com/pgrandinetti/traffic-macrosimulatorThis project is an open-source MATLAB package that can create and simulate road networks objects described by macroscopic dynamics. The technical details can be found and explored further in my Ph.D. thesis.
Katie DJ
https://github.com/pgrandinetti/katiedjKatie DJ is the first free traffic data broadcast. It is a web platform that streams data related to road network traffic (e.g., number of vehicles in the streets). The data is open and accessible to everyone via WebSockets without needing any other type of information such as email.
Compiler Learning Language Tool
https://github.com/pgrandinetti/compilersThis is a new, minimalist programming language that I built from scratch. The language was designed as a tool to learn compiler theory. I spoke about the theory and practice of compilers and how I built the language's toolkit in a series of articles published at the link below.
LegalKite
LegalKite is the first legal annotation platform for professionals in the Swiss legal industry. I developed part of the back end; the main goal was to optimize the search through laws and annotations.
Education
2014 - 2017
PhD in Optimization of Large Scale Networks
Grenoble Institute of Technology - Grenoble, France
2011 - 2013
Master's Degree in Automation Engineering
University of Calabria - Rende, Italy
2008 - 2011
Bachelor's Degree in Computer Engineering
University of Calabria - Rende, Italy
Certifications
JUNE 2022 - PRESENT
TensorFlow: Advanced Techniques Specialization
Coursera
NOVEMBER 2019 - PRESENT
TensorFlow in Practice
Deeplearning.ai (via Coursera)
JUNE 2018 - PRESENT
Data Science
Johns Hopkins University (via Coursera)
JANUARY 2018 - PRESENT
Deep Learning
Deeplearning.ai (via Coursera)
SEPTEMBER 2014 - PRESENT
Machine Learning
Stanford University (via Coursera)
Skills
Libraries/APIs
TensorFlow, Pandas, React
Tools
MATLAB, Docker Swarm, Terraform
Languages
R, Python, SQL, Java, C, Haskell, Go
Frameworks
Flask, Django, Django Channels
Paradigms
REST, Search Engine Optimization (SEO)
Platforms
Amazon Web Services (AWS), Docker, AWS Lambda, Linux, Vanta
Storage
PostgreSQL, Elasticsearch, Oracle RDBMS, Amazon DynamoDB
Other
Freelancing, APIs, Data Science, Optimization, Amazon RDS, FastAPI, Deep Learning, Machine Learning, Site Reliability Engineering (SRE), Natural Language Processing (NLP), Aimsun, Big Data, Large-scale Computing, Data Privacy, Writing & Editing, Simulations, WebSockets, Generative Pre-trained Transformers (GPT), Fintech, SOC 2, Languages, Compilers
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