Senior Engineer - AI

We are looking for a Senior Engineer who is comfortable working in AI-augmented development environments and can effectively leverage modern coding assistants and agentic tools to deliver high-quality software. What You'll Do Work in hybrid engineering teams where humans and AI agents collaborate as part of the same delivery workflow Stay in the loop for key responsibilities: Setting direction and technical strategy Quality assurance and validation of AI-assisted outputs Critical decision-making and maintaining accountability for outcomes Managing client relationships and translating business needs into solutions Guide implementation by combining hands-on development with AI-assisted execution Break down complex problems into structured tasks that can be executed by the hybrid team Contribute to defining best practices and workflows for effective human + AI collaboration Identify opportunities where AI can accelerate delivery while ensuring human expertise drives reliability WE HAVE MANY THINGS TO OFFER! Flexible schedule, international projects, home office kit, healthcare and more, you name it. Check out the whole list of benefits on our dedicated page, by clicking the following link: Benefits Nice To Have Have AI-Augmented Development Tools Coding assistants & agentic IDEs GitHub Copilot (inline completion + Copilot Chat / Copilot Agent mode) Cursor IDE agentic, multi-file editing with natural language instructions Windsurf (Codeium) agentic coding with Cascade JetBrains AI Assistant relevant for Java/.NET leads already on IntelliJ/Rider Continue.dev open-source, self-hosted alternative (relevant for security-conscious clients) Agentic coding / task automation Claude Code terminal-based agentic coding Kilo Code VS Code extension for agentic, multi-step coding tasks; open-source fork of Cline with strong local model support OpenCode terminal-native AI coding agent, model-agnostic; relevant for engineers who prefer CLI-first workflows or self-hosted setups GitHub Copilot Agent mode Devin, SWE-agent (awareness-level) Practices & Mindset (the more differentiating signal) Prompt engineering for code generation writing effective, context-rich prompts; iterating on AI output rather than accepting it blindly AI-assisted code review using LLM tools to pre-screen PRs, catch patterns, suggest refactors Test generation with AI leveraging Copilot/Cursor to scaffold unit and integration tests Context engineering structuring repos, READMEs, and architecture docs so AI tools can reason over them effectively (this is the senior/lead differentiator) Agentic workflow design ability to break tasks into agent-executable steps; understanding when to use human-in-the-loop vs. autonomous execution LLM & AI Platform Familiarity OpenAI API / Azure OpenAI Anthropic API (Claude) AWS Bedrock or Google Vertex AI (for DevOps/cloud leads) LangChain or LlamaIndex basic understanding of RAG and chain patterns AI-Augmented DevOps / Platform (for DevOps leads specifically) AI-assisted IaC generation (Copilot + Terraform, Pulumi AI) GitHub Actions with AI steps or LLM-based pipeline stages Monitoring/observability tools with AI anomaly detection (Datadog AI, AWS DevOps Guru)