§ 01Snapshot
- Levelsenior / lead backend engineer, about a decade server-side. Not a CTO, and doesn't present as one.
- Roles heldbackend lead of a large consumer gaming platform · solo backend engineer on a monolith-to-microservices migration · DevOps engineer at an analytics SaaS · Kotlin backend engineer on peer-to-peer crypto exchange products
- Scaleled a cross-functional team of about ten (frontend, backend, QA, DevOps) · platform with more than a million monthly users, hundreds of integrated games · public API at thousands of requests per second
- Industriesonline gaming and entertainment · consumer platforms inside messengers · fintech and payments · crypto / Web3 · trading analytics SaaS
- Company typesventure-backed consumer platform (scale-up) · early-stage product startup · mid-size SaaS
- Specialtiespayment services and gateway failover · DDoS and abuse resilience · real-time state sync over WebSockets · cloud cost reduction (managed DB → self-hosted, warehouse cost tuning) · trunk-based delivery and safe CI/CD for non-engineers · analytics event pipelines
- Best forseed startup · scale-up · corporate innovation team (especially teams where non-engineers ship with AI tools)
- Core stackKotlin (Spring, Spring Boot, WebFlux, JPA, jOOQ, Flyway), Python, PostgreSQL, Kafka, ClickHouse, BigQuery, Netty WebSockets, GCP / GKE (Kubernetes), AWS (CloudFormation, Beanstalk), GitLab CI, JWT
§ 02Services
| ✓ | PR/MR code review | Kotlin / JVM, Python backends |
| ✓ | Architecture review | |
| ✓ | Architecture design | |
| ✓ | Audit / due diligence | backend, infra, cost |
| ✓ | Vibe-code rescue | safe delivery pipeline first, then backend hardening |
| ✓ | Team mentoring |
§ 03Track record
An isolated CI/CD sandbox so non-technical "vibe coders" can test ideas safely
The marketing team wanted to test product hypotheses themselves, increasingly with AI-generated code, but any change near production was risky.
One of our experts built an isolated CI/CD sandbox: a separate environment and pipeline where non-engineers could deploy and try their ideas, fully cut off from production, while engineering kept trunk-based development on the main line.
Marketing could experiment on their own without touching prod, and the engineering team stopped being the bottleneck for every small test.
Proprietary payment services next to highly available third-party gateways
A platform with more than a million monthly users needed in-app payments that kept working when an external provider had a bad day.
One of our experts built the platform's own payment services and integrated third-party payment gateways in a high-availability setup.
Payments ran through services the team owned, with external gateways set up so that one failing provider did not stop revenue.
Mitigating large distributed DDoS attacks on a high-traffic API
A public API serving thousands of requests per second was hit by massive distributed attacks.
One of our experts led the backend response: hardening the API layer and the surrounding cloud setup on Kubernetes so attack traffic could be absorbed and filtered.
The attacks were mitigated and the platform stayed online for its players.
Managed cloud database → self-hosted Postgres on Kubernetes
The managed cloud database was a growing line on the cloud bill and queries were slower than they needed to be.
One of our experts moved the database from the managed service into self-hosted Postgres running in the team's Kubernetes cluster, and took over monitoring of the whole cloud setup.
Cloud spend went down and queries got roughly a third faster.
Analytics pipeline from dozens of mini apps, plus warehouse cost tuning
Product analytics had to be collected from dozens of embedded mini apps without slowing the apps down or blowing up warehouse costs.
One of our experts built an event pipeline from Kotlin services through Kafka into a cloud data warehouse, then balanced workloads between the warehouse and a columnar database to control cost.
One analytics stream for the whole catalogue, with warehouse spend under control.
A platform engine for dozens of bots with real-time state sync
A messenger-based gaming platform needed one core engine to run dozens of bots and keep game state in sync for players in real time.
One of our experts built the core platform engine that managed the bots, with real-time state synchronisation over WebSockets on Netty.
One shared engine powered the platform's bots and live state for a large player base.
Solo migration of a PHP monolith to Kotlin microservices
A mobile game's PHP monolith was holding back development.
One of our experts migrated it alone to Kotlin microservices (Spring Boot, WebFlux, Kafka) on Kubernetes, with trunk-based development and feature toggles so the switch could happen step by step.
The product moved to a service architecture without a big-bang rewrite.
Release deploys cut from hours to minutes
Each release took hours of manual deployment work.
One of our experts, working as the DevOps engineer, automated the cloud setup with infrastructure-as-code and custom monitoring metrics.
Release deployment went from hours to minutes.
Backend for peer-to-peer crypto exchange bots
A peer-to-peer crypto exchange running inside a messenger needed a backend that handled real funds safely.
One of our experts wrote most of the production code: integration with blockchain nodes, hot wallets, and fiat-to-crypto exchange flows in Kotlin/Spring.
A working exchange product that moved real money between users.
Leading a ten-person cross-functional team with no turnover
A fast-moving platform team (frontend, backend, QA, DevOps) was under constant business pressure.
One of our experts led the team of about ten, ran the technical and management interviews, and acted as a buffer between business demands and the engineers to prevent burnout.
The team stayed together; nobody left during their time as lead.
§ 04What you can book
A safe "sandbox + guardrails" setup for a founder who ships with AI
A founder or a non-engineering team generates features with AI tools and deploys straight to production.
Set up a separate sandbox environment and pipeline for experiments, protect the main branch with automated checks, and define how an experiment gets promoted to production after review.
A working sandbox, a written promotion path from experiment to production, and a short list of the riskiest areas in the current backend.
Kotlin / JVM PR review for an AI-assisted backend team
A small team writes much of its Kotlin/Spring code with AI assistants and nobody senior has time to read every PR.
Per-PR review focused on transaction boundaries, payment idempotency, Kafka consumer behaviour, jOOQ/JPA query patterns and migration safety, with comments that explain the why.
Reviewed PRs, plus a short recurring note on patterns the AI keeps getting wrong in this codebase.
Cloud cost and resilience review for a GCP / Kubernetes startup
The cloud bill grows faster than usage and nobody is sure the platform would survive a traffic spike or an attack.
Walk the managed-service bill, database and warehouse workloads, autoscaling and edge protection, and compare self-hosting vs managed for the biggest items.
A prioritised list of cost and resilience fixes, each with its trade-off and rough effort.