Web Software Engineer (Full-Stack) with AI Expertise

• 3+ years of professional experience building web or product applications.
• Strong command of at least one of: PHP (Laravel, Symfony or similar), JavaScript/TypeScript (Node.js, React, Vue or similar), Python, Go.
• Solid full-stack fundamentals: APIs, relational databases, caching, deployment.

You are comfortable across layers, not only in one.

• Hands-on, fluent use of AI-assisted and agentic tools (Claude Code, Cursor, GitHub Copilot or similar) in real delivery work.

These tools are part of how you build, not something you've tried a few times.

• Using AI to understand and navigate an unfamiliar codebase or stack;
• Multi-step agentic development (planning, implementation, verification);
• AI-assisted debugging, refactoring and test generation;
• Prompt engineering and context setup for development workflows (project instructions, rules, reusable prompts).
• Ability to work independently and drive tasks to production with little supervision.
• Upper-Intermediate English (B2) or higher.
• A proactive mindset and clear communication with the team and clients.
• Build and ship web applications and features from idea to production, in PHP, JavaScript/TypeScript, Python or Go.
• Set up and maintain development environments, build pipelines and deployments.
• Consciously use AI tools in your daily workflow
• Integrate AI capabilities into client products where relevant: LLM APIs, RAG, agent and tool workflows.
• Take part in technical design and code reviews, and help build reusable practices for working with AI across different stacks.
• Work closely with project managers, analysts and clients.

Understand not only what to build, but why it matters for the business.

• Production experience in two or more of our languages.
• Practical experience with low-code, integration and automation platforms (e.g. n8n, Workato, Boomi, Celigo, OutSystems) or data integration tools (e.g.

Airbyte).

• Cloud experience with AWS, Azure, Kubernetes.
• Message brokers (RabbitMQ, Kafka) and observability tools (ELK, Prometheus, Grafana).
• Building AI features into products: RAG pipelines, vector databases, embeddings, semantic search.