Hi 👋, I'm a Scala Software Engineer at ING Belgium
with 6+ years of JVM expertise and a strong ML/DL background. ITMO University graduate specializing in back-end development, distributed systems, low-level system software, functional programming and deep learning applications.
☎️ Contact information
👨💻 Work experience
Scala Software Engineer
*ING Belgium, Luxembourg – January 2024 → Present time*
My current position at ING Belgium involves development and maintenance of the bank's in-house SWIFT message processing system. Main focus is on performance and reliability of message routing, implementing new SWIFT message types and formats. Among other things, up until now, my main tasks were:
- Tackling distributed tracing, across several services, communication protocols and concurrency execution contexts
- Automated Kubernetes inter-pod load-testing utility with custom DSL defining execution plans
Java Software Engineer & DevOps
Arhs Developments, Luxembourg — May 2023 → January 2024
At Arhs Developments my position was twofold:
- As a Java Developer on EU customs project:
- Modernized legacy Java apps, making them deployable on cloud clusters
- Developed and Integrated a custom Kubernetes operator for Oracle WebLogic
- As a DevOps on CBAM project:
- Built CI/CD, managed Docker images, created Kubernetes deployments configurations
- Automated testing and releasing processes for CBAM initiative
Scala Software Engineer
TINKOFF Bank, St. Petersburg — Jul 2019 → May 2023
As a Scala Developer at Tinkoff, I've developed several internal services for marketing platform:
- In-house marketing data statistics system, for real time data aggregation and analysis:
- Handling 5TB+ of data with frequent OLAP queries and low response times
- Numerous optimizations and reimplementation of older version of a service, led to 300% speed optimization on pre-aggregation stage, whilst not sacrificing queries response times
- Extensible architecture allowed for additional implementation of extensions, with fixed aggregation plans, for extra-fast data retrieval
- Notifications router:
- Implementation of service, which handles filtering, routing and categorization of all notifications Tinkoff clients receive
- Domain-specific language for definition of specific rules, on which to process the notifications
- Mobile app Stories backend:
- Implementation and support of Stories backend engine, with such features like: statistics aggregation, personal ranking, contextual content filtering & etc.
- Low-latency provision of dozen stories, to each of 2M daily users, opening an app
- Polls service back-end:
- Development of custom polls service with template engine
- Service features: personal context integration, access management, SEO, statistics aggregation, custom complex reply logic (e.g. like pick-a-paths)
- AI Replies clusterization and analysis, based on factual and emotional semantics of text replies
- Implementation of custom MLOps platform with ML algorithm speed and memory optimizations that enabled on-demand analysis generation, increasing speed by 10x compared to initial MVP versions
📚 Education
Master's Degree in Artificial Intelligence
ITMO University - St. Petersburg
Thesis: Neural Network-Based Optimization of Video Compression Through Frame Group Length Prediction
Course: Deep Learning and Generative Artificial Intelligence
Graduated with distinction
Bachelor's Degree in Computer Science
ITMO University - St. Petersburg
Thesis: Development of Distributed Embedded Key-Value Database for JVM
Course: System and Applied Software
Graduated with distinction
💻 Technical Skills Overview
Tech stack experience
- Scala (6+ yrs): backend, tagless-final, Typelevel stack; Akka, ZIO; Spark (ETL)
- Python: ML, data processing, MLOps, research
- Java/JVM: extensive, multi-language
- Also: Haskell; Clojure (eDSL, async channels); C/Assembly (OS, kernel, networking); Kotlin; Rust; TypeScript; JavaScript (React, Svelte)
- Deep learning: PyTorch, TensorFlow (production)
- MLOps: Python/Scala services for ML deployment (Tinkoff)
Data, storage, and messaging
- SQL: PostgreSQL, Oracle
- NoSQL: Redis, Aerospike, Cassandra, MongoDB
- Analytics/Search: ClickHouse, Elasticsearch
- Messaging: Kafka, NATS
- Graph: Neo4j
- Embedded: RocksDB (custom distributed KV store)
- Infrastructure and DevOps
- Containers/Orchestration: Docker, Kubernetes (deployments, custom operators)
- Linux: advanced CLI, admin, shell scripting
- CI/CD: Jenkins pipelines (design and implementation)
- Networking: TCP/IP, security, troubleshooting
Selected results
- OLAP system over multi-terabyte datasets; high-frequency pre-aggregations and granular analytics
- Integrated Java-native distributed tracing with Scala reactive libraries
- Implemented distributed consensus and data replication (bachelor’s thesis)
- Built OS components: bootloader, kernel, virtual memory, threading
- Production MLOps services: Scala backends + Python ML models