Sergey Grebenshchikov, Developer in Munich, Bavaria, Germany
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Sergey Grebenshchikov

Verified Expert  in Engineering

Software Developer

Munich, Bavaria, Germany
Toptal Member Since
July 15, 2019

Since 2017, Sergey has been working as a consultant and engineer on DevOps projects for clients like Adidas, Siemens, Audi, and Tesla. He's acquired high-level design skills as well as hands-on development experience with major cloud platforms, containerized deployment targets (Docker/Kubernetes/ECS), and a variety of CI/CD infrastructures. He communicates clearly, efficiently, and proactively—ensuring the transparency and relevance of each task.


Tesla, Inc.
Shell, Continuous Delivery (CD), Linux, DevOps, Continuous Deployment, Helm...
Syncier GmbH (via Codecentric AG)
Shell, RxJS, Vuex, Vue, Jenkins, Kubernetes, Docker, Go
Siemens AG (via Codecentric AG)
Bash Script, Shell, Amazon EC2, Continuous Delivery (CD)...




Preferred Environment

Zsh, Git, Visual Studio Code (VS Code), MacOS

The most amazing...

...project I've designed and developed was a symbolic execution based fuzzer/test generator and specification language for industrial control software.

Work Experience

DevOps Engineer

2019 - 2020
Tesla, Inc.
  • Designed and developed a greenfield CI/CD infrastructure, and migrated applications to it.
  • Built extensions for upstream tools to adapt them to client-specific workflows.
  • Set up logging and monitoring for infrastructure and applications, both via configuration of existing software as well as via custom tooling.
Technologies: Shell, Continuous Delivery (CD), Linux, DevOps, Continuous Deployment, Helm, Kubernetes, Docker, Terraform, Go, Infrastructure as Code (IaC)

Consultant | Product Engineer

2019 - 2019
Syncier GmbH (via Codecentric AG)
  • Developed back-end services and front-end applications for compliance-aware cloud resource management.
Technologies: Shell, RxJS, Vuex, Vue, Jenkins, Kubernetes, Docker, Go

DevOps Consultant

2018 - 2019
Siemens AG (via Codecentric AG)
  • Ensured compliance with InfoSec requirements for an existing cloud-based CI/CD stack.
  • Simplified and modularized Terraform codebase, and defined module interfaces, enabling multiple teams to share and collaborate on infrastructure code with minimal overhead.
  • Developed various infrastructure tools in Go to support integrations with Terraform, Docker, AWS ECR, AWS Secrets Manager, Jenkins, and SonarQube.
  • Simplified and refactored Ansible-based provisioning of EC2 instances.
  • Replaced most dynamic provisioning operations with static images (Docker images and EC2 AMIs).
Technologies: Bash Script, Shell, Amazon EC2, Continuous Delivery (CD), Amazon Web Services (AWS), Linux, DevOps, Continuous Deployment, Ansible, Jenkins, GitLab, Packer, Terraform, Docker, Go, Infrastructure as Code (IaC)


2018 - 2018
Audi Business Innovation GmbH (via Codecentric AG)
  • Defined the technical SLIs and outlined the SLAs for an existing centralized CI/CD platform, together with a role/responsibility allocation minimizing organizational friction.
  • Performed a quantitative technical analysis of CI/CD performance and availability issues, and a qualitative analysis of organizational constraints and bottlenecks.
Technologies: Continuous Delivery (CD), DevOps, Continuous Deployment, Kubernetes, Docker, Jenkins, Go, R

Software Engineer

2017 - 2018
MX1 (via Codecentric AG)
  • Migrated an existing video-streaming app to Kubernetes; was responsible for the design, implementation, and task prioritization.
  • Conducted a performance analysis and developed the monitoring hooks and metrics.
  • Implemented extensions and refactored the app’s video processing pipeline.
Technologies: Bash Script, Shell, Google Cloud Platform (GCP), Continuous Delivery (CD), Linux, DevOps, Continuous Deployment, Grafana, Prometheus, Kubernetes, Docker, FFmpeg, C, Go

DevOps Consultant

2017 - 2017
Adidas AG (via Codecentric AG)
  • Worked extensively on Kubernetes cluster infrastructure, built cluster-level/application-level monitoring using Prometheus, Grafana, cAdvisor, as well as application-specific custom metric collectors.
  • Taught courses on Docker and Kubernetes for developers and assisted teams with their transition to a DevOps workflow.
  • Developed project starter templates for Java (Spring Boot) and Node.js (Express.js) to facilitate recommended testing practices and provide immediate integration with the CI/CD environment and monitoring components.
Technologies: Bash Script, Shell, Amazon EC2, Continuous Delivery (CD), Amazon Web Services (AWS), Linux, DevOps, Continuous Deployment, Elasticsearch, Kibana, Prometheus, Grafana, Express.js, Spring Boot, Jenkins, JavaScript, Java, Go, Kubernetes, Docker, Infrastructure as Code (IaC)

A tool that generates terminal plots from JSON data (line plot, bar chart, scatter plot, heatmap, and histogram).

A tiny linear interpolation library for Go.

This is a tool that checks for updates of external terraform modules referenced in the given Terraform source. It enables semantic versioning of Git-sourced Terraform modules, a feature otherwise only available when using modules from Terraform registries.

Rich argument types for the Go standard library "flag" package.

A self-contained, Docker-aware filesystem event reactor.

Semantic versioning CLI—retrieval, querying, and an MVS constraint graph solver.

A TCP connection latency analysis tool.

Synthesizing Software Verifiers from Proof Rules

This paper was published in Proceedings of the 33rd ACM SIGPLAN Conference on Programming Language Design and Implementation.

In this paper, we presented a method for the automatic synthesis of software verification tools. Our synthesis procedure takes input (a description of the employed proof rule, e.g., program safety checking via inductive invariants) and produces a tool. This tool then automatically discovers the auxiliary assertions required by the proof rule, e.g., inductive loop invariants and procedure summaries.

We rely on a (standard) representation of proof rules using recursive equations over the auxiliary assertions. The discovery of auxiliary assertions—i.e., solving the equations—is based on an iterative process that extrapolates solutions obtained for the finitary unrollings of equations. We show how our method synthesizes automatic safety and liveness verifiers for programs with procedures, multi-threaded programs, and functional programs.

Our experimental comparison of the resulting verifiers with existing state-of-the-art verification tools confirms the practicality of the approach.
2012 - 2015

Master of Science (MSc) Degree in Informatik (Computer Science)

Technische Universität München - München, Germany

2008 - 2012

Bachelor of Science (BSc) Degree in Informatik (Computer Science)

Technische Universität München - München, Germany


Vue, D3.js, FFmpeg, Libav, Amazon EC2 API, Atomic CSS, Vuex, RxJS, JMX


Terraform, Shell, Jenkins, Amazon Elastic Container Service (Amazon ECS), Docker Compose, Grafana, AWS CodeBuild, Packer, Helm, NGINX, AWS CloudFormation, Git, Zsh, GitLab, AWS Fargate, GitLab CI/CD, Google Kubernetes Engine (GKE), Kibana, Amazon CloudFront CDN, Ansible


Bash Script, Go, Java, OCaml, SQL, F#, C, JavaScript, R, Python


Drone CI, Amazon Web Services (AWS), Docker, Kubernetes, Amazon EC2, Linux, MacOS, Google Cloud Platform (GCP), JVM, Visual Studio Code (VS Code)


Iterative Development, Continuous Deployment, Continuous Integration (CI), DevOps, Agile Software Development, Functional Programming, Continuous Delivery (CD)


Redis, PostgreSQL, SQLite, Elasticsearch, Amazon S3 (AWS S3), Google Cloud Storage


Tachyons CSS, Spring Boot, Express.js


Site Reliability Engineering (SRE), CI/CD Pipelines, Infrastructure as Code (IaC), Prometheus, Simplicity, Presentations, Foreign Function Interfaces (FFI), Amazon Route 53, Domain Name System (DNS), Transport Layer Security (TLS), PKI

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