Maksim Smirnov
SUMMARY
- Cut credit approval time from 3 days to 24 hours and reduced Kubernetes infrastructure costs by 30% as Principal Technical Lead at Raiffeisenbank. 10+ years designing event-driven distributed systems and leading multi-team engineering organisations in fintech, enterprise, and startup environments. Based in Serbia — remote-first, EU-friendly hours. Open to Staff/Principal Engineer or Engineering Manager roles.
EDUCATION
MIPT University,
Master in Data Science
- Data Entanglement in Machine Learning
ITMO University,
Bachelor in Information Security
- Thesis: Developed a neural network for detecting steganographic content in digital images.
CERTIFICATIONS
AWS Certified Solutions Architect
- Validated expertise in designing and deploying scalable cloud systems on AWS.
IELTS 7.5
- Certified English language proficiency at fluent level.
EXPERIENCE
Principal Technical Lead, Raiffeisenbank
- Directed development of an enterprise-scale credit automation platform, coordinating six engineering teams across microservices and event-driven architecture (Kafka, gRPC, REST).
- Architected migration to event-driven system with Kafka, reducing Kubernetes infrastructure costs by 30% and improving system observability via Prometheus and Grafana.
- Shortened credit approval time from 3 days to under 24 hours through pipeline redesign and CI/CD automation.
- Mentored senior engineers and defined hiring criteria and technical interview process for team scaling.
Senior Software Engineer, Yandex
- Maintained Python library porting pipeline for Yandex’s air-gapped internal package ecosystem: evaluated upstream OSS packages, authored compatibility patches, and integrated them into Yandex’s proprietary VCS and package registry.
- Engineered automation tooling to reduce manual overhead in the import and patching workflow, enabling internal teams to track and adopt new upstream releases reliably.
Engineering Manager, Runity,
contract, concurrent
- Improved project estimation accuracy, reducing variance by 10–15% through structured planning and delivery observability.
- Achieved zero attrition while delivering six previously stalled projects by establishing clear ownership and mentoring practices.
- Founded and launched company-sponsored technical training programs.
Engineering Manager, Metamap,
contract, concurrent
- Reduced codebase by 50%, cutting release cycles from several days to hours via microservices decomposition and CI/CD pipeline overhaul.
- Led architectural migration from monolith to microservices (Python, REST, Docker/Kubernetes), enabling product expansion into Latin America and Singapore.
Lead Software Developer, EPAM Systems
- Led agile teams of 8–9 developers on pharmaceutical software projects, managing delivery, code quality, and stakeholder communication.
- Founded and ran a Python Competence Center, delivering technical lectures, workshops, and mentoring to 100+ engineers.
- Designed technical hiring and assessment frameworks, significantly expanding the engineering talent pool.
Co-Founder and Lead Python Developer, Aglaya
- Co-founded and led a team of 10 engineers, delivering custom information systems for enterprise clients.
PROJECTS
Python Competence Center,
Founder and Instructor
- Developed curriculum and facilitated hands-on training for Python developers.
- Increased team proficiency and standardized technical evaluation protocols.
Metrics: Jira Engineering Analytics Toolkit,
Author and Maintainer
- Created an open-source toolkit for analyzing and visualizing software engineering metrics from Jira.
- Implemented calculations for cycle time, lead time, queue time, throughput, and other key metrics.
- Designed the toolkit to be modular, extensible, and fully tested.
- Source on GitHub
PUBLICATIONS
Software Engineering Metrics,
Medium, 2024
- Outlined practical metrics for software teams using Jira data.
- Demonstrated methods to identify bottlenecks and visualize delivery trends.
- Explained how to use metrics for forecasting and process improvement.
- Read on Medium
Profiling Asynchronous Python,
Medium, 2023
- Described common performance bottlenecks in asynchronous Python applications, including blocking calls and context switching.
- Compared deterministic and statistical profilers, highlighting strengths of tools such as Scalene for async code.
- Provided guidance on interpreting profiler results and optimizing asynchronous Python performance.
- Read on Medium
SKILLS
Programming Languages
- Python, Go, C++, Clojure
Databases
- PostgreSQL, MongoDB, Redis
Cloud and DevOps
- AWS, Docker, Kubernetes, Helm, Terraform, GitLab CI/CD, Prometheus, Grafana
Messaging and APIs
- Kafka, RabbitMQ, gRPC, REST
Frameworks and Tools
- LiteStar, Gin, FastAPI, Django, PyTorch, LangChain, Ollama
Practices
- System Design, Distributed Systems Architecture, Technical Leadership, Mentoring, Hiring
Languages
- English (Fluent), German (Beginner), Russian (Native)