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Backend Developer (Golang)

  • Remote.Bulgaria
  • Remote.Georgia
  • Remote.Kazakhstan
  • Remote.Poland
  • Алмати
  • Астана
  • Белград
  • Варна
  • Варшава
  • Вроцлав
  • Днепър
  • Ереван
  • Киев
  • Клуж-Напока
  • Краков
  • Ларнака
  • Лвов
  • Лодз
  • Люблин
  • Одеса
  • Рига
  • София
  • Тбилиси
  • Харков
Гореща позицияМалък екип (1-10 души)

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Client

Our client is an international SaaS company developing tools that empower businesses to create highly engaging digital experiences. Their solutions are trusted by thousands of organizations worldwide to collect, analyze, and act on user data effectively.

Наемаме ви в компанията, не в проект

Project overview

You will work on a product-led SaaS platform focused on creating interactive and conversational digital experiences for businesses. The platform enables users to build engaging forms and surveys through features such as logic branching, workflow automation, payment-enabled forms, and AI-powered form generation. The project supports scalable data collection and helps companies optimize lead generation and workflow processes.

Position overview

We are looking for a backend engineer who builds agentic systems for a living — not someone exploring agents on the side. You will design and ship agentic components, MCP servers, and the Go services that back them. You investigate technical approaches independently and use AI coding agents as a core part of how you work, not as a novelty.

You should be equally comfortable reasoning about a tricky PostgreSQL migration and about why an agent loop is failing on the third tool call. This role gives you direct influence over backend architecture and over how AI capabilities are extended across the platform.

In your application, include one short paragraph on an agent, MCP server, or AI feature you have shipped — what it does, the hardest problem you solved, and how you knew it was working. Generic "I've used Cursor" answers will be filtered out.

Responsibilities

  • Design, build, and operate agentic components: tool definitions, multi-step loops, retries, guardrails, and evaluation.
  • Build and maintain MCP servers and the tooling around them.
  • Use AI coding agents (Claude Code, Cursor, Copilot, or equivalent) as a primary part of your workflow to accelerate delivery without compromising quality.
  • Maintain and improve existing backend services written in Go.
  • Design schemas, optimize queries, and manage safe database migrations in PostgreSQL.
  • Investigate technical approaches and propose alternatives for solving platform challenges.
  • Collaborate with product and engineering contributors on feature design and implementation.
  • Participate in code reviews and contribute to engineering best practices.
  • Support integration efforts across frontend, backend, and infrastructure components.

Requirements

  • Production experience building agents, agentic tools, or MCP servers — not just prototypes or demos. Be ready to walk through a system you built: tool design, failure modes, how you evaluated and improved it.
  • Hands-on with at least one major LLM provider's API (Anthropic, OpenAI, AWS Bedrock, or equivalent), including streaming, tool use, and structured outputs.
  • Daily use of AI coding agents in your workflow. You can articulate concretely where they help, where they hurt, and how you maintain code quality and review discipline.
  • Strong experience developing backend services using Go.
  • Experience with PostgreSQL including schema design, performance tuning, and migration management.
  • Ability to independently analyze problems, evaluate trade-offs, and propose technical solutions.
  • Experience participating in Agile development teams.
  • Fluency in English for documentation and collaboration.

Nice to have

  • Experience with AgentCore.
  • Experience designing evaluation pipelines for LLM features (offline evals, LLM-as-judge, human review loops).
  • Experience with Python or Node.js for backend work.
  • Experience building frontend features using React.
  • Experience working with cloud environments or CI/CD pipelines.
  • Open-source contributions, published writing, or talks on applied AI engineering.

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