Full Stack Developer (Golang and React/TypeScript) at DataArt – UA | Jobyssey

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.

Project overview
The project is an interactive form builder designed for marketers, researchers, HR, sales, and customer experience teams to create engaging, conversational online forms, surveys, and quizzes.

Key features:
Visually appealing conversational design optimized for high completion rates
AI-powered data interpretation
Workflow and logic builders for personalized experiences
Brand customization options
Seamless integrations with various tools to automate data collection and streamline workflows

Position overview
We are seeking a Full Stack Engineer who builds with AI in the loop — both as a daily development practice and as a product capability. You are fluent with coding agents (Claude Code, Cursor, Copilot) and ship faster because of them, not despite them. You understand how to design, prompt, evaluate, and operate LLM-backed features in production — including model selection, context engineering, evals, guardrails, and cost and latency tradeoffs.

Beyond AI, you have deep Golang and React/TypeScript expertise and own features end-to-end: from understanding business requirements, through writing and testing code, to deployment and production monitoring. This role requires strong autonomy, proactive communication, and the ability to deliver reliable, scalable solutions in a modern cloud environment.

This position operates according to the CET time zone.

Responsibilities
Design and ship LLM-powered product features: prompt design, retrieval, tool and function calling, structured outputs, streaming, evaluation harnesses, and guardrails.
Operate AI features in production: monitor quality, latency, cost, and failure modes; iterate on prompts, models, and architectures based on real usage.
Use AI coding tools (Claude Code, Cursor, Copilot) as a core part of your workflow to accelerate delivery without compromising code quality.
Design, develop, and maintain backend services using Golang and frontend applications using React/TypeScript.
Collaborate with product managers, designers, and other engineers to translate business requirements into technical solutions.
Implement event-driven architectures leveraging Kafka for real-time data processing.
Manage and optimize AWS environments, ensuring scalability and reliability.
Define and maintain infrastructure using Terraform and deploy services in Kubernetes clusters.
Build and maintain CI/CD pipelines in Jenkins for automated deployments.
Ensure high-quality code through comprehensive testing and code reviews.
Monitor application performance and troubleshoot issues in production environments.
Contribute to UI/UX improvements and ensure seamless integration between frontend and backend.

Requirements
Demonstrable production experience shipping LLM-powered features. Be ready to walk through a feature you built: prompts, evaluation approach, failure modes you fixed, and how you measured success.
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 assistants in your workflow; able to articulate where they help, where they hurt, and how you maintain code quality.
Strong, in-depth knowledge of Golang.
Experience delivering complex backend systems from design to production.
Solid background with Kafka and event-driven architectures.
Hands-on experience managing AWS environments.
Proficiency with Terraform for infrastructure-as-code.
Experience deploying and operating services in Kubernetes.
Building CI/CD pipelines in Jenkins.
Proven ability to ensure quality through comprehensive testing.
Strong proficiency in React and TypeScript.
Experience contributing to front-end features, UI components, and client-side logic.
Ability to seamlessly integrate frontend with backend services.
Excellent English communication skills, written and spoken.
Self-driven and able to work independently while managing workload effectively.

Nice to have
Experience with agent frameworks, multi-step tool-use loops, or RAG systems at scale.
Built or maintained an evaluation pipeline (offline evals, LLM-as-judge, human review loops).
Open-source contributions, published writing, or talks on applied AI engineering.

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