Software, data, and machine learning engineer with professional IT experience since 2016.

Strongly technical leader with proven expertise in designing and evolving enterprise-wide architectures. Experienced in driving cross-team collaboration, establishing architectural standards, and leading systems adoption at company scale. Track record of making strategic technical decisions that balance business needs with long-term maintainability, scalability, and operational excellence.

Participated in building many company-wide systems from scratch, including:

• data platforms for Data Scientists
• data quality systems
• data observability and governance (data catalogue, feature stores and data lineage)
• data modeling

Involved in many infrastructure projects (using pulumi/terraform IAC):

• LLMs, including building an LLM load balancer and router
• Data lakes
• APIs
• Medallion data architecture
• Full mlops lifecycle (including cicd, monitoring - data dog, A/B testing, AWS lambda, ECS, ECR, DynamoDb and many other technolgies)

Extensive experience with:

• Apache Spark
• Performance optimization
• Data ingestion (batch and streaming)
• Designing Data Lakes/Warehouses
• Databricks, AWS, CI/CD, ML/DevOps
• IaC

Strong expertise in software architectures, distributed systems, concurrent programming, and event-driven architectures.

Primary language: Python. Also having experience with Scala. Functional programming principles applied across most projects.

Strong advocate for software testing. Focus areas: solving complex problems, designing system architectures, and building frameworks.

Passionate about software development, dedicating free time to reading and continuous learning. Preference for in-depth technical books to gain deep understanding of technologies.

Maintaining multiple OSS projects, including:

• pramen-py https://github.com/AbsaOSS/pramen/tree/main/pramen-py - A framework enabling convenient definition of Spark transformation pipelines.

• pytest-when https://github.com/zhukovgreen/pytest-when - A project helping BDD testing in a very convenient way

• friendly-sequences https://github.com/zhukovgreen/friendly-sequences - A small but powerful type-safe library for function chaining.

• Talks repository - https://github.com/ZhukovGreen/talks (contains many educational sessions about data lakes, python and testing)
• PyCon CZ 2023 - Can we have a better feature store https://cz.pycon.org/2023/program/talks/65/
• DevConf.US 2021 - Framework for integration tests lifecycle https://www.youtube.com/watch?v=K7VcLnHRz0w&list=PLU1vS0speL2ZbTPg-aU2Rw2s6IPsTVoCF&index=60
• PyAmsterdam 2020 - Using asyncio for building CLI applications https://py.amsterdam/2020/10/15/virtual-pyamsterdamnowtzzoneinfoeuropeamsterdam-stayathome.html
• PyCon CZ 2018 - HVAC engineer and Python https://youtu.be/KAZn2Fhh7f4?t=324

Dates Employed Feb 2025 - present

Building various infrastructure projects around LLMs Mentoring the team in the software architectures principles Solving complex design changes and developing the architecture of new systems

Dates Employed Aug 2022 - present

Building data platform for data scientists.

Databricks on AWS is the main platform. Pulumi / Terraform for infrastructure. Python is the primary language. Spark and Delta Lake.

Key responsibilities:

• Setting standards for team software development practices
• Building data platform
• Enabling data quality system
• Developing data transformation framework with metadata driven schema evolution and many other nice features
• Automating Databricks pipeline deployment
• Managing feature store, data contracts, and table metadata
• Optimizing data transformations and debugging spark jobs at scale
• Processing streaming and batch data
• Working with big data, Delta Lakes, data ingestion, and data catalogs

Dates Employed Aug 2021 - Aug 2022

Developed and built components of an on-premise system for convenient big data ETL processes, with abstractions around the data warehouse for data scientists (feature-centric interfaces). Also developed various data transformations for different projects and maintained an existing project.

Dates Employed Jul 2021 - Aug 2021

Worked on the Convert2RHEL team. Designed a simple but specific and rich CI system for developing and running integration tests (libvirt, Ansible, Testing Farm, tmt). Developed new features in the upstream project, performed code reviews, adopted pytest, transitioned the app to Python 3, and provided mentoring.

A note from the promotion document:

Artem joined Red Hat during the spring of 2020 as a software developer working on
the LEAPP team. Artem’s enthusiasm for Python and pythonic development practices
soon led him to adopt an advocacy role on his team.
Artem transitioned to the Convert2RHEL team in early 2021, and rapidly became one
of the team’s most prolific contributors. He continued to broaden his reach beyond
his SST by creating a hardware deprecation database and associated microservice
which helps to take the mystery out of hardware support.
Artem’s creativity and energy have made him a true asset to his teams.

Dates Employed Apr 2020 - June 2021

Worked on the OS & App Modernization team (OAMG).

Primary responsibilities:

• Maintained and contributed to OAMG repositories https://github.com/oamg
• Developed a data delivery system (internal framework to distribute various data to clients)
• Worked on convert2rhel utility (new features and CI)

Dates Employed Jan 2016 – Jan 2020

Building a software platform to support new products and company processes.

Dates Employed May 2006 – Aug 2016

Worked in various positions within the HVAC industry:

• Compact Air Handling Units (AHU) project manager (~1 year)
• AHU technical support (~1 year)
• HVAC designer (~5 years)
• Energy modeler for LEED certification (~1 year)
• Technical supervisor on site (~1 year)
• Ventilation systems installer (~1 year)

CS229: Machine Learning Dates: 2016 – 2017

Completed all lecture videos and keynotes, resolved all assignments. Course syllabus: http://cs229.stanford.edu/syllabus.html

Machine Learning Engineer Nanodegree Dates: 2016 – 2018

https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009

Degree Name Master’s Degree Field Of Study Mechanical Engineering (HVAC) Grade M.Sc. in heating, ventilation, air conditioning systems Dates attended or expected graduation 2002 – 2008

This is my primary base education. A lot of mathematics, physics, and drawings.

• Udacity: PyTorch Scholarship Challenge from Facebook
• AWS trainings (Big Data, Data Lakes, Developing with AWS)
• A vast amount of different courses at Udemy/Coursera, such as data structures and algorithms, functional programming, PyTorch Reinforcement learning, etc.

• Russian - native
• English - good professional level
• Czech - good professional level