Pietro is available for hire

Pietro Grandinetti

Verified Expert in Engineering

Data Science Developer

Milan, Metropolitan City of Milan, Italy

Toptal member since November 20, 2017

Expertise

Amazon RDS FastAPI Python MATLAB Flask Docker Amazon Web Services (AWS)Django R Deep Learning Machine Learning PostgreSQL Pandas C Java

Bio

Pietro is a systems engineer with a professional background as a software developer and a PhD in large-scale optimization, from the Technology Institute of Grenoble. Equally at ease in applied science and software positions, he enjoys designing and prototyping systems in Python, R, and C using web and data science tools. He likes new challenges and loves lifelong learning.

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

Python - 8 years Data Science - 7 years PostgreSQL - 7 years APIs - 7 years Docker - 5 years Amazon Web Services (AWS) - 4 years Machine Learning - 4 years Go - 4 years

Availability

Part-time

Preferred Environment

Linux, C, Python, Amazon Web Services (AWS), Go, PostgreSQL

The most amazing...

...thing I've built was a distributed, efficient, mixed integer optimization algorithm to design the optimal schedule of traffic lights in large cities.

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 process and procedures, technological security, business continuity, and data protection and privacy. No control exceptions were noted from the auditors' side.
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, with no security risks found.
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

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

Experience

Portability | The Future Of Financial Data

https://www.forbes.com/sites/forbestechcouncil/2021/12/01/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

https://www.forbes.com/sites/forbestechcouncil/2021/07/06/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-macrosimulator

This 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/katiedj

Katie 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.

Plume | Compiler Learning Language Tool

https://github.com/pgrandinetti/compilers

This 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 Plume 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 Degree 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

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)

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