Peter Gagarinov

Peter Gagarinov

CTO | PhD | Head of Development, Leadership in AI


With over two decades of immersive experience in the IT sphere, particularly in AI/ML, DevOps, and FinTech, I am thrilled at the prospect of channeling my accumulated expertise and passion into innovative endeavors.

As CTO at Potential Energy LLC, I lead the creation and launch of a cutting-edge, cloud-based SaaS platform, richly embedded with AI and ML capabilities. This venture didn’t just reinforce my technical prowess in AI/ML; it significantly bolstered my leadership skills, enabling me to guide my teams toward the fruition of ambitious, technologically advanced objectives.

In my role as Head of DevOps and Backend Development at All of Us Financial, I was instrumental in integrating the latest technologies into our backend systems, significantly enhancing our financial platforms’ efficiency and reliability. My strategic focus on these technological advancements played a pivotal role in positioning the company as a valuable acquisition by PayPal.

My subsequent role at PayPal as Head of DevOps/Cloud Architect allowed me to leverage my previous experiences, integrating modern technologies into much larger systems. This position not only broadened my technical knowledge base but also enriched my understanding of applying cloud and ML technologies to FinTech industry, fostering innovation and adding value on a worldwide scale.

In my professional journey, which includes numerous successful projects at Allied Testing LLC, I has had a comprehensive exposure to AI/ML technologies. From hands-on development to strategic oversight, these experiences have sharpened my ability to lead teams towards the conceptualization and subsequent execution of impactful AI/ML solutions.

  • Technical Leadership
  • Software Engineering
  • Artificial Intelligence
  • DevOps
  • Advanced Deep Learning, 2020 - 2021

    Deep Learning School, Phystech School of Applied Mathematics and Informatics

  • PhD in Mathematics and Computer Science, 2014

    Lomonosov Moscow State University, the faculty of Computational Mathematics and Cybernetics

  • MSc in Mathematics and Computer Science, 1998 - 2003

    Lomonosov Moscow State University, the faculty of Computational Mathematics and Cybernetics








Programming Languages Skills



Technical Leadership
Software Engineering
Full Stack Development
AI/Machine Learning
Project Management
Generative AI
Financial Markets
Trading Systems
Volatility Trading
Big Data
Data Analysis
Financial Risk
Mathematical Modeling
Technical Computing
Distributed Databases
Relational Databases
NoSQL Databases
Geospatial Tools


AWS Certified Solutions Architect – Associate
Earners of this certification have a comprehensive understanding of AWS services and technologies. They demonstrated the ability to build secure and robust solutions using architectural design principles based on customer requirements. Badge owners are able to strategically design well-architected distributed systems that are scalable, resilient, efficient, and fault-tolerant.
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AWS Knowledge: Serverless
Earners of this badge have developed technical skills and knowledge of AWS serverless concepts and services with a focus on AWS Lambda and Amazon API Gateway.
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Ignite Summit: Speaker
The owner of this badge has presented a tech talk for developers and architects at one of the Ignite Summits.
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Advanced Deep Learning: parts I, II, the first degree diploma
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Cours intensifs de français
Project Management Fundamentals


Advanced cloud-based AI-driven SaaS platform for US real estate market (2022-present)
An advanced cloud-based SaaS platform, currently serving the US real estate market with a focus on geo-analytics and forecasting. It integrates state-of-the-art Generative AI and Machine Learning technologies, enhancing both functionality and accuracy. The platform operates natively with Amazon Web Services (AWS) and Kubernetes, supporting multi-tenancy and ensuring robust security. Its key features include auto-scalability and high availability, providing real estate professionals with reliable, data-driven insights and predictions.
Automated Talent Acquisition Platform (2022-2023)
The Automated Talent Acquisition Platform offers insightful, AI-driven capabilities to optimize our recruitment process. It swiftly navigates through job applications, allowing our HR team to allocate less time to administration and more time to strategic decision-making. By enhancing efficiency and accelerating the hiring process, our solution contributes to a more productive talent acquisition strategy, fostering the continued growth and success of our company
Internal DevOps Training (2022-2023)
This free DevOps course provides an extensive look at Kubernetes, blending on-premises and cloud solutions with a focus on industry best practices. It is designed for both beginners and experienced developers, covering key aspects like development process optimization, application deployment, and infrastructure scaling. The course offers deep insights into Kubernetes, preparing learners for a successful tech industry career. Organized into well-structured lessons, it includes a wealth of resources and code snippets from various external repositories. This course is an invaluable opportunity for those seeking to enhance their DevOps skills and industry knowledge.
Alliedium AIssistant (2020-2022)
ML-based automated issue management, defect classification and bug triaging.
Awesome Linux Configuration Scripts (2020-present)
A curated set of awesome configuration scripts for various Linux-based systems.
Awesome Software Engineering (2020-present)
A curated list of awesome software engineering resources.
ML-based Clustering System for Large Data Sets (2019)
Iterative clustering algorithm operating on large dataset not fitting into RAM, processing took days.
Ellipsoidal Toolbox for MATLAB (2013-2018)
Ellipsoidal Toolbox for MATLAB is a standalone set of easy-to-use configurable MATLAB routines and classes to perform operations with ellipsoids and hyperplanes of arbitrary dimensions.
MxBerry Core (2014-2018)
Core library for boosting a development in Matlab. The library was originally developed as an auxilary library for Ellipsodal Toolbox for Matlab project back in 2014.
PgMex (2014)
PgMex is a high-performance PostgreSQL client library for Matlab that enables a Matlab-based application to communicate with PostgreSQL database in the Matlab native way by passing data in a form of matrices, multi-dimensional arrays and structures. The library is written in pure C which gives a significant performance boost for both small and data-heavy database requests. Both Windows and Linux platforms are supported.
Equity Deep Learning/StatArb Portfolio Management System (2016–2018)
Deep learning-based semi-automatic trading system for US stock market. The system automatically extracts a sensible information (in form of features for a deep learning model) from both publicly-available and subscription-based sources of daily and high-frequency market data. After a second stage of dimensionality reduction and features re-combination (via back-testing & cross-validation) the most relevant features are then used to train a complex non-linear model on GPU. The portfolio optimization-driven trading strategy uses probabilistic forecasts made by the model and current positions on the market to generate specific instructions for the traders.
Equity Index Volatility and Correlation Trading System (2013–2015)
ML-based semi-automatic market making and position trading system utilizing statistical arbitrage opportunities in volatility index – equity index future spreads on US market.
Risk Analytics Engine (2015–2016)
Broker-side stress-testing and optimization system for both aggregating and scaling multiple traders/strategies operating with options, futures, ETFs and stocks into a single portfolio for a better profitability/risk ratio for a broker.
Equity/Index Options StatArb Portfolio Management System (2008-2013)
Semi-automatic trading system built for US options market making and position trading using a relative value implied volatility modeling based on a statistical forecasting of co-movements of implied volatility surfaces.
Algorithmic Trading Development Environment: BEST Studio (2010–2012)
Simulation platform for trading finance. It can simulate exchange functionality, exchange behavior, smart order routers, client behavior, market data sources and all interactions between them with a high degree of realism and consistency. A high-frequency exchange simulator uses a set of ML-based models to replicate a realistic behavior of the market. An integrated order-matching engine allows for tested trading strategies be surrounded by a realistic trading environment which can simulate various stress-testing scenarios (including exotic ones) while still keeping things realistic.
High Fidelity Exchange Simulator (2007–2008)
Exchange Simulator built for major European investment bank program trading desk which models reaction of market participants on user intrusions (activity that is absent in historical data files) by replaying historical orders flow, processing orders submitted by users according to exchange rules and modeling the market response on these user orders using probability-driven empirically justified models. The simulator uses a set of ML-based models to replicate a realistic behavior of the market. Having a wide range of behavioral settings allows to simulate various (including exotic) scenarios while still keeping things realistic due to an integrated order matching logic and fine-tuned ML models.
Tick Data Storage & Analysis project (2007–2009)
Infrastructure built for US proprietary trading firm for storing and analyzing tick data and order entry data in backtesting and real-time modes, providing an environment for defining and operating value-added metrics calculations in real-time and on historical time spans.
Energy StatArb Portfolio Management System (2004–2007)
Energy StatArb Portfolio Management System. Semi-automatic trading system built for US energy hedge fund for generating trading recommendations on the market of energy/energy resources futures based on methods of Theory of Probability, Mathematical Statistics and Theory of Graphs, Time Series Analysis.
Implementation Shortfall model (2006–2007)
Model built for major European investment bank program trading desk provides an approach towards calculating optimal order execution schedule based on “implementation shortfall” (ISF) minimization.
Market Impact Model (2006–2007)
Model built for major European investment bank program trading desk provides an approach towards making a quantitative estimates of influence of a market participant on market itself.
Tick data comparison tool (2004)
The tool developed for major European investment bank program trading desk provides means for comparing tick data recorded from multiple sources thus allowing estimating a tick data relative quality.
Behavioral testing environment for proprietary intraday trading system (2003–2004)
The environment built for Major European investment bank provides means for backtesting of trading strategies incorporated into existing intraday trading system realizing arbitrage opportunities by trading with combinations of equity baskets and equity index futures.

Recent Publications

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