Peter Gagarinov

Head of Software Development | PhD | AI, Cloud and Engineering Leadership

Biography

With over two decades of experience in the IT sphere, particularly in AI/ML, DevOps, and FinTech, I build systems where advanced analytics meet production engineering at scale.

I am Head of Software Development at Highview Power, based in London, where I lead the development of trading and market modelling systems for long duration energy storage. Highview is building the world’s largest commercial scale liquid air energy storage platform at Carrington near Manchester, alongside further projects at Hunterston and Killingholme.

Before Highview, I served as Chief Technology Officer at a US based advanced data analytics and AI solutions provider, where I led the design and launch of a cloud based SaaS platform for the US real estate market, focused on geo-analytics and forecasting. The platform integrated generative AI and ML capabilities and was built natively for AWS and Kubernetes, with multi-tenancy, security, auto-scalability and high availability as first order concerns.

As Head of DevOps and Backend Development at All of Us Financial, I oversaw the design and implementation of the Kubernetes native backend for an online trading platform and led the adoption of continuous integration and delivery on AWS. The company was subsequently acquired by PayPal, where I went on to lead DevOps architecture and strategy for the PayPal Invest cloud platform.

At Allied Testing I spent more than eighteen years across two spells, most recently as Head of AI Transformation, leading the integration of artificial intelligence into products and processes, and earlier as Head of Modeling and Analytics, where I led research and development teams building quantitative models and trading systems.

Alongside my industry work, I designed and delivered the specialized course AI-Driven Expert Systems at the Technical University of Moldova, covering fine-tuning and retrieval for domain specific AI systems across 180 hours of theory and practice. I hold a PhD in applied mathematics and computer science from Lomonosov Moscow State University and continue to co-maintain the Ellipsoidal Toolbox for MATLAB, which grew out of that research.

Interests

Technical Leadership
Software Engineering
Artificial Intelligence
Energy Market Modelling
DevOps

Education

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

Languages

English

Proficient/Advanced

French

Intermediate

Russian

Native

Programming Languages Skills

Python

TypeScript

Java

Julia

Rust

Matlab

C/C++

Skills

Technical Leadership

Software Engineering

Full Stack Development

AI/Machine Learning

DevOps

Project Management

CI/CD

Cloud

Containers

Kubernetes

MLOps

Generative AI

Agile

Finance

Statistics

Financial Markets

Trading Systems

Equities

Derivatives

Options

Volatility Trading

Big Data

Data Analysis

Financial Risk

Mathematical Modeling

Technical Computing

Distributed Databases

Relational Databases

NoSQL Databases

Geospatial Tools

Accomplishments

AWS Certified Solutions Architect – Associate

Amazon Web Services Training and Certification Feb 2024 – Feb 2027

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

Amazon Web Services Training and Certification Oct 2023 – Oct 2023

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

GridGain Aug 2022 – Aug 2022

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

Deep Learning School, Phystech School of Applied Mathematics and Informatics Sep 2020 – Mar 2021

202111002964968471

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Cours intensifs de français

Lomonosov Moscow State University, the faculty of Pedagogical Education Feb 2017 – May 2017

5317a9055

Project Management Fundamentals

IBM Feb 2006

ECM-03466/6

Projects

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.