Mark Andreev

Senior Software Engineer

// Contacts Linkedin Github
// Phone: +4407458900595
// Development Stack Java. Spring: MVC, Data, AMQP, Kafka, Integration, Batch, Security, State machine, Apache Camel, Vert.x, GraalVM. Python. Pandas, Scikit-learn, XGBoost, LightGBM, Catboost, Matplotlib, Tornado, FastAPI, Flask Data Processing. Spark, Flink, Cassandra, Hadoop, Kafka, PostgreSQL. Third party. PostgreSQL, Clickhouse, Kafka, Keycloak, MongoDB, RabbitMQ, Redis, Prometheus, Docker, Kubernetes, Helm, Linux, Airflow Cloud. AWS {EC2, S3, RDS, CloudFront, SQS, SNS, Lambda, Batch, IAM, Registry}; Azure {VM, BLOB, Registry}
// Experience summary

Accomplished Senior Software Engineer, 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.

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.

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.

// Experience Man Group plc, 2024 - now Senior Software Engineer, May 2024 - now, 2017 - 2024 Senior Software Engineer, Sep 2022 - May 2024
  • 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
  • Create performance regression tests (Gatling) | Cover 80% of queries that eliminates major performance degradations
  • Create performance optimizations for kafka subscription proxy (java 21 virtual threads, shared subscription) | Decrease CPU load 5x times
  • Implement security improvements for Platform (audit, L4 network policies, L7 network filter) | Apply security IS requirements at network level
  • Cover platform’s services with health performance metrics (Prometheus, Grafana, Alerts) | Decrease issue investigation time 3x times

[Java, Python, Query optimizations, Load tests, Kafka, Clickhouse, Kubernetes, Feature store, Spring, Vert.x, GraalVM]

Senior Software Engineer, Sep 2019 - Aug 2022
  • Migrate feature store to Kafka & Clickhouse (Column OLAP DB) | Increase query speed 15x times
  • Create low level connectors for Industrial Data Exchange formats (MQTT, OPC UA, Historian) | Decrease CPU load to exchange server 3x times
  • Migrate model serving runtime to Kubernetes (KubeAPI, Helm)
  • Deploy platform to AKS (Azure Cloud) & K3s (on premisses, no internet)

[Java, Python, Kubernetes, Helm, Kafka, Clickhouse, Azure, AKS, OLAP, Feature store, Spring]

Middle Software engineer, Nov 2017 - Sep 2019
  • Create feature store for sensor’s time series data (Java, Spring, PostgreSQL, TimescaleDB)
  • Create model serving runtime server (Python, Processes)
  • Create incident management service (Java, Spring, State machines)
  • Create ETL based on S3, SQS, S3 SFTP

[Java, Spring, Python, AWS, ETL, Feature store, PostgreSQL, TimescaleDB, State machines, S3, SQS, S3 SFTP]

Junior Machine learning engineer, May 2017 - Oct 2017
  • Airline data clusterization. Create approach for data splitting for offline AB tests
  • Telecom data chron. Create solution for offline chron scoring based on telecom data activity
  • Web data gender detection. Create solution for offline gender detection based on web activity
  • Mobile data geo analysis. Create reports about geo activity based on mobile location data
  • Timeseries data for Industrial data. Create data pipeline for failure prediction

[Python, PySpark, Spark, SQL, Scikit learn, Pandas, XGBoost]

Big Data Indicators Internship, Oct 2016 - May 2017 Machine Learning Engineer
  • Create data collection & processing pipeline
  • Use topic models for discover trends
  • Create sentiment analysis models for trend prediction

[Data mining, Python, MongoDB, Redis, Machine learning, NLP, Topic models]

// Education Lomonosov Moscow State University Master's degree. Computational Mathematics and Cybernetics Moscow Power Engineering Institute Bachelor's degree. Institute of automatics and computer science
// Publications 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, Oct 2017 DATA ANALYTICS AND MANAGEMENT IN DATA INTENSIVE DOMAINS, DAMDID / RCDL’2017, Andreev M. Big Data approach to measure inflation expectations: the case of the Russian economy, Jul 16, 2017 IFABS 2017 Oxford Conference, Goloshchapova I., Andreev M. Measuring inflation expectations ofthe Russian population with the help of machine learning. Voprosy Ekonomiki. 2017;(6):71-93. (In Russ.), Goloshchapova I., Andreev M.
// Contribute to Open Source
  • Keycloak
  • Apache Camel
    • CAMEL-16092 Add fix for header override by Azure Storage Blob download consumer.
  • Apache Ignite
    • IGNITE-13713 Implemented target encoding preprocessor.
    • IGNITE-13714 Implemented catboost inference integration.
    • IGNITE-13386 Implemented new distances (BrayCurtis, Canberra, JensenShannon and etc).
  • Clickhouse
  • Zipkin
    • Implemented experimental PostgreSQL storage.
  • Tornado Swagger
    • Swagger API Documentation builder for tornado server.