Backend Developer (Go)

 Posted 14 days ago
  
 Cyprus
  
5-10 years experience
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AI Summary

Design and build core backend services using Go and DDD to process millions of images for ML inference and dashboards. Own the data layer using PostgreSQL and ClickHouse while managing deployments on EKS.

We’re hiring for a Raw Ventures portfolio company — a computer vision platform that processes millions of images from real-world deployments and turns them into actionable insights for industry partners. Series A.


The role

You own architecture and core services for a system that flows millions of images into ML inference (CV team) and out to dashboards and partner integrations. DDD-driven, PostgreSQL + ClickHouse for data, your own k8s — no separate DevOps.


Stack

Go • PostgreSQL • ClickHouse • Prefect • AWS • EKS • S3 • Docker • Claude Code 


What you’ll do

  • Design service architecture with DDD — bounded contexts, aggregates, ubiquitous language
  • Build core services in Go
  • Own the data layer: PostgreSQL + ClickHouse (analytics, time-series)
  • Build analytics on ClickHouse: detection aggregates, heat maps, time-series
  • Operate services on EKS — deploy, debug, scale
  • Build the spec-driven dev process for AI coding agents


What we expect

  • 5+ years backend, strong Go
  • Strong DDD applied in real systems
  • Strong architectural thinking — service boundaries, data ownership, consistency, scale
  • Hands-on Claude Code in production
  • Spec-driven development — built processes around AI agents
  • Deep PostgreSQL
  • Columnar / analytics DB experience (ClickHouse, BigQuery, Redshift, Snowflake)
  • Production Docker, Kubernetes, AWS
  • Russian — fluent or native (required)
  • English B1+
  • Central European working hours


Nice to have

TypeScript • ML serving (Triton, TorchServe) • CV basics • Deep ClickHouse (materialized views, sharding) • Image/video pipelines • IoT/edge • Scientific software or systems where the real world interferes with data


What we offer

  • Fully remote, CET hours
  • Real product impact at scale
  • Direct contact with leadership and CV team
  • AI-augmented development culture
  • Competitive compensation, discussed individually

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