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<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Mark Andreev (Senior Software Engineer)</title> <meta name="description" content="Machine Learning Engineer"> <meta name="robots" content="index, follow"> <link rel="apple-touch-icon" sizes="144x144" href="upload/img/icons/apple-touch-icon.png"> <link rel="icon" type="image/png" sizes="32x32" href="upload/img/icons/favicon-32x32.png"> <link rel="icon" type="image/png" sizes="16x16" href="upload/img/icons/favicon-16x16.png"> <link rel="manifest" href="site.webmanifest"> <link rel="mask-icon" href="upload/img/icons/safari-pinned-tab.svg" color="#5bbad5"> <meta name="msapplication-TileColor" content="#da532c"> <meta name="msapplication-TileImage" content="upload/img/icons/mstile-144x144.png"> <meta name="theme-color" content="#ffffff"> <style> a { color: darkblue; } .page { font-family: monospace; margin-left: 0; margin-top: 0.5em; margin-right: 0; font-size: 1.2em; } .header { display: flex; justify-content: center; text-align: center; margin-bottom: 3em; } .header-title { justify-content: center; } .title { font-size: 3rem; margin-top: 0; margin-bottom: 0; } .title__main-word { color: #133cff; } .subtitle { font-size: 1.2rem; margin-top: 0; margin-bottom: 0; } @media (min-width: 600px) { .body { display: grid; grid-template-columns: 1fr; } } @media (min-width: 800px) { .body { display: grid; grid-template-columns: 1fr 1fr; grid-gap: 10px; } } @media (min-width: 1000px) { .body { display: grid; grid-template-columns: 1fr 1fr 1fr; grid-gap: 10px; } } .body-col { width: 100%; } .block { margin-left: 1em; margin-right: 1em; margin-bottom: 3em; } .block-title { display: block; font-size: 1.1rem; margin-bottom: 0.5rem; } .block-item { display: block; padding-left: 0.5em; margin-bottom: 0.5em; text-align: justify; } .block-item:after { content: ""; display: inline-block; } .block-item-sm { font-size: 0.9em; } </style> </head> <body> <div class="page"> <div class="header"> <div> <div class="header-title"> <div> <picture> <source srcset="upload/img/avatar-100x100.webp" type="image/webp"/> <img class="d-block mx-auto mb-4 rounded" src="upload/img/avatar-100x100.png" alt="" width="100" height="100"/> </picture> </div> <div><h1 class="title"><span class="title__main-word">M</span>ark Andreev</h1> <h2 class="subtitle">Senior Software Engineer</h2></div> </div> </div> </div> <div class="body"> <div class="body-col"> <div class="block"> <span class="block-title">// Contacts</span> <span class="block-item"><strong><a href="mailto:mark.andreev@gmail.com">mark.andreev@gmail.com</a></strong></span> <span class="block-item"> <a href="https://linkedin.com/in/mark-andreev-75a753134">Linkedin</a> <a href="http://github.com/mrk-andreev">Github</a> </span> <br/> <span class="block-item"> // CV: <a href="/upload/resume/MarkAndreev-SeniorSoftwareEngineer-1.pdf">English</a> </span> <span class="block-item"> // Phone: +4407458900595 </span> </div> <div class="block"> <span class="block-title">// Blog</span> <span class="block-item"> <a href="/blog/deep-dive-into-apache-parquet-format" target="_blank">Deep dive into Apache Parquet Format</a> </span> <span class="block-item"> <a href="/blog/how-test-java-code-structure-in-unit-tests">How test Java code structure in unit tests (archunit)</a> </span> <span class="block-item"> <a href="/blog/java-two-way-ssl">Java Two Way SSL Client (+ Spring example)</a> </span> <span class="block-item"> [ <a href="/snippets">All snippets</a>, <a href="/snippets/external_links">External publications</a> ] </span> </div> <div class="block"> <span class="block-title">// Talks</span> <span class="block-item"> <a href="/upload/talks/ml-in-production_funtech-ml.pdf">"ML in production" at the FunTech ML-meetup</a> </span> <span class="block-item"> <a href="/upload/talks/streaming-vs-batching_conundrum-meetup.pdf">"Streaming vs Batching" at the Conundrum Meetup</a> </span> </div> <div class="block"> <span class="block-title">// Projects</span> <span class="block-item"> <a href="/snippets/gitignore-generator/">[App] .gitignore generator</a> </span> <span class="block-item"> <a href="https://github.com/mrk-andreev/demo-tsplayer">[Demo] Time series player</a> </span> <span class="block-item"> <a href="https://github.com/mrk-andreev/demo-offline-lock">[Demo] Offline lock with Redis</a> </span> <span class="block-item"> <a href="http://bigdata-indicators.com/">Bigdata Indicators</a> </span> <span class="block-item"> <a href="https://github.com/mrk-andreev/tornado-swagger">Tornado-swagger</a> </span> <span class="block-item"> <a href="https://enduringnet.org/about/">EnduringNet</a> </span> </div> <div class="block"> <span class="block-title">// Certificates</span> <span class="block-item"> <a href="/upload/certs/CockroachDB_Query_Performance_for_Developers.pdf">CockroachDB Query Performance for Developers</a> </span> <span class="block-item"> <a href="/upload/certs/Redis_for_Java_Developers_Redis_University.pdf">Redis for Java Developers</a> </span> <span class="block-item"> <a href="/upload/certs/Mark-Andreev-S201-Data-Modeling-and-Application-Development-Data-Modeling-Course-Completion-Certificate-Scylla-University.pdf">ScyllaDB. Data Modeling and Application Development</a> </span> <span class="block-item"> <a href="/upload/certs/AWS_Well-Architected_Training.pdf">AWS Well-Architected Training</a> </span> <span class="block-item"> Deep Dive into AWS <a href="/upload/certs/Deep_Dive_into_Amazon_Simple_Storage_Service_(Amazon_S3).pdf">S3</a>, <a href="/upload/certs/Deep_Dive_into_Amazon_Glacier.pdf">Glacer</a>, <a href="/upload/certs/Deep_Dive_into_Amazon_Elastic_File_System_(EFS).pdf">EFS</a> </span> <span class="block-item"> <a href="/upload/certs/Deep_Dive_on_Container_Security.pdf">Deep Dive on Container Security</a> </span> </div> <div class="block"> <span class="block-title">// Development Stack</span> <span class="block-item"> <strong>Java</strong>. Spring: MVC, Data, AMQP, Kafka, Integration, Batch, Security, State machine, Apache Camel, Vert.x, GraalVM. </span> <span class="block-item"> <strong>Python</strong>. Pandas, Scikit-learn, XGBoost, LightGBM, Catboost, Matplotlib, Tornado, FastAPI, Flask </span> <span class="block-item"> <strong>Data Processing</strong>. Spark, Flink, Cassandra, Hadoop, Kafka, PostgreSQL. </span> <span class="block-item"> <strong>Third party</strong>. PostgreSQL, Clickhouse, Kafka, Keycloak, MongoDB, RabbitMQ, Redis, Prometheus, Docker, Kubernetes, Helm, Linux, Airflow </span> <span class="block-item"> <strong>Cloud</strong>. AWS {EC2, S3, RDS, CloudFront, SQS, SNS, Lambda, Batch, IAM, Registry}; Azure {VM, BLOB, Registry} </span> </div> </div> <div class="body-col"> <div class="block"> <span class="block-title">// Experience summary</span> <span> <p>Accomplished Senior Software Engineer at Conundrum, London, specializing in optimizing ML systems and MLOps with Java, Python, Kubernetes, Spring, and Vert.x for Kafka & Clickhouse feature stores. Also proficient in PostgreSQL and Redis. Recognized for significant achievements, including a 3x improvement in popular queries, a 5x reduction in CPU load for Kafka subscription proxies, and a stellar 15x increase in query speed. I bring a deep understanding of trade-offs, balancing performance enhancements with resource efficiency.</p> <p>Actively contributing to open-source projects such as Keycloak, Apache Camel, Apache Ignite, Clickhouse, Zipkin, and Tornado Swagger, with diverse contributions from implementing a fix for header override by Azure Storage Blob download consumer to advanced features like target encoding preprocessor and CatBoost inference integration.</p> <p>Armed with a Master’s Degree in applied mathematics and computer science from Lomonosov Moscow State University, I am passionate about driving innovation in machine learning technologies.</p> </span> <span class="block-title">// Experience</span> <span class="block-item"><strong><a href="https://conundrum.ai/">Conundrum.ai</a></strong>, 2017 - now</span> <span class="block-item">Senior Software Engineer, Sep 2022 - now</span> <span class="block-item block-item-sm"> <ul> <li>Implement low level optimization for feature store on top of Kafka & Clickhouse (projections, application level query planner) | Increase one of the most popular queries 3x times</li> <li>Create performance regression tests (Gatling) | Cover 80% of queries that eliminates major performance degradations</li> <li>Create performance optimizations for kafka subscription proxy (java 21 virtual threads, shared subscription) | Decrease CPU load 5x times</li> <li>Implement security improvements for Platform (audit, L4 network policies, L7 network filter) | Apply security IS requirements at network level</li> <li>Cover platform’s services with health performance metrics (Prometheus, Grafana, Alerts) | Decrease issue investigation time 3x times</li> </ul> <p> [Java, Python, Query optimizations, Load tests, Kafka, Clickhouse, Kubernetes, Feature store, Spring, Vert.x, GraalVM] </p> </span> <span class="block-item">Senior Software Engineer, Sep 2019 - Aug 2022</span> <span class="block-item block-item-sm"> <ul> <li>Migrate feature store to Kafka & Clickhouse (Column OLAP DB) | Increase query speed 15x times</li> <li>Create low level connectors for Industrial Data Exchange formats (MQTT, OPC UA, Historian) | Decrease CPU load to exchange server 3x times</li> <li>Migrate model serving runtime to Kubernetes (KubeAPI, Helm)</li> <li>Deploy platform to AKS (Azure Cloud) & K3s (on premisses, no internet)</li> </ul> <p> [Java, Python, Kubernetes, Helm, Kafka, Clickhouse, Azure, AKS, OLAP, Feature store, Spring] </p> </span> <span class="block-item">Middle Software engineer, Nov 2017 - Sep 2019</span> <span class="block-item block-item-sm"> <ul> <li>Create feature store for sensor’s time series data (Java, Spring, PostgreSQL, TimescaleDB)</li> <li>Create model serving runtime server (Python, Processes)</li> <li>Create incident management service (Java, Spring, State machines)</li> <li>Create ETL based on S3, SQS, S3 SFTP</li> </ul> <p> [Java, Spring, Python, AWS, ETL, Feature store, PostgreSQL, TimescaleDB, State machines, S3, SQS, S3 SFTP] </p> </span> <span class="block-item">Junior Machine learning engineer, May 2017 - Oct 2017</span> <span class="block-item block-item-sm"> <ul> <li>Airline data clusterization. Create approach for data splitting for offline AB tests</li> <li>Telecom data chron. Create solution for offline chron scoring based on telecom data activity</li> <li>Web data gender detection. Create solution for offline gender detection based on web activity</li> <li>Mobile data geo analysis. Create reports about geo activity based on mobile location data</li> <li>Timeseries data for Industrial data. Create data pipeline for failure prediction</li> </ul> <p> [Python, PySpark, Spark, SQL, Scikit learn, Pandas, XGBoost] </p> </span> <span class="block-item"><strong>Big Data Indicators</strong> Internship, Oct 2016 - May 2017</span> <span class="block-item">Machine Learning Engineer</span> <span class="block-item block-item-sm"> <ul> <li>Create data collection & processing pipeline</li> <li>Use topic models for discover trends</li> <li>Create sentiment analysis models for trend prediction</li> </ul> <p> [Data mining, Python, MongoDB, Redis, Machine learning, NLP, Topic models] </p> </span> </div> </div> <div class="body-col"> <div class="block"> <span class="block-title">// Education</span> <span class="block-item"><strong>Lomonosov Moscow State University</strong></span> <span class="block-item">Master's degree. Computational Mathematics and Cybernetics</span> <span class="block-item"><strong>Moscow Power Engineering Institute</strong></span> <span class="block-item">Bachelor's degree. Institute of automatics and computer science</span> </div> <div class="block"> <span class="block-title">// Publications</span> <span class="block-item"> <strong>A New Approach to Determining the Attitude of Authors of Short Texts to the Topics Discussed in the Texts on the Example of Estimating the Inflations Expectations</strong>, Oct 2017 DATA ANALYTICS AND MANAGEMENT IN DATA INTENSIVE DOMAINS, DAMDID / RCDL’2017, Andreev M. </span> <span class="block-item"> <strong>Big Data approach to measure inflation expectations</strong>: the case of the Russian economy, Jul 16, 2017 IFABS 2017 Oxford Conference, Goloshchapova I., Andreev M. </span> <span class="block-item"> <strong>Measuring inflation expectations ofthe Russian population with the help of machine learning</strong>. Voprosy Ekonomiki. 2017;(6):71-93. (In Russ.), Goloshchapova I., Andreev M. </span> </div> <div class="block"> <span class="block-item">// Contribute to Open Source</span> <span class="block-item block-item-sm"> <ul> <li> <strong>Keycloak</strong> <ul> <li><a href="https://github.com/keycloak/keycloak/pull/8717">KEYCLOAK-19743</a> Fix null username in ldap.</li> </ul> </li> <li> <strong>Apache Camel</strong> <ul> <li><a href="https://github.com/apache/camel/pull/4949">CAMEL-16092</a> Add fix for header override by Azure Storage Blob download consumer.</li> </ul> </li> <li> <strong>Apache Ignite</strong> <ul> <li><a href="https://github.com/apache/ignite/pull/8466">IGNITE-13713</a> Implemented target encoding preprocessor.</li> <li><a href="https://github.com/apache/ignite/pull/8489">IGNITE-13714</a> Implemented catboost inference integration.</li> <li><a href="https://github.com/apache/ignite/pull/8197">IGNITE-13386</a> Implemented new distances (BrayCurtis, Canberra, JensenShannon and etc).</li> </ul> </li> <li> <strong>Clickhouse</strong> <ul> <li><a href="https://github.com/ClickHouse/ClickHouse/pull/37228">ClickHouse-37228</a> Remove group.id from StorageKafka::createWriteBuffer.</li> </ul> </li> <li> <strong>Zipkin</strong> <ul> <li>Implemented experimental PostgreSQL storage.</li> </ul> </li> <li> <strong>Tornado Swagger</strong> <ul> <li>Swagger API Documentation builder for tornado server.</li> </ul> </li> </ul> </span> </div> <div class="block"> <span class="block-item">// Social Media</span> <div> <a href="https://twitter.com/MrkAndreev/"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24"><path d="M24 4.557c-.883.392-1.832.656-2.828.775 1.017-.609 1.798-1.574 2.165-2.724-.951.564-2.005.974-3.127 1.195-.897-.957-2.178-1.555-3.594-1.555-3.179 0-5.515 2.966-4.797 6.045-4.091-.205-7.719-2.165-10.148-5.144-1.29 2.213-.669 5.108 1.523 6.574-.806-.026-1.566-.247-2.229-.616-.054 2.281 1.581 4.415 3.949 4.89-.693.188-1.452.232-2.224.084.626 1.956 2.444 3.379 4.6 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