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 DoWork in hybrid engineering teams where humans and AI agents collaborate as part of the same delivery workflowStay in the loop for key responsibilities:Setting direction and technical strategyQuality assurance and validation of AI-assisted outputsCritical decision-making and maintaining accountability for outcomesManaging client relationships and translating business needs into solutionsGuide implementation by combining hands-on development with AI-assisted executionBreak down complex problems into structured tasks that can be executed by the hybrid teamContribute to defining best practices and workflows for effective human + AI collaborationIdentify opportunities where AI can accelerate delivery while ensuring human expertise drives reliabilityWE 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: BenefitsNice To Have HaveAI-Augmented Development ToolsCoding assistants & agentic IDEsGitHub Copilot (inline completion + Copilot Chat / Copilot Agent mode)Cursor IDE agentic, multi-file editing with natural language instructionsWindsurf (Codeium) agentic coding with CascadeJetBrains AI Assistant relevant for Java/.NET leads already on IntelliJ/RiderContinue.dev open-source, self-hosted alternative (relevant for security-conscious clients)Agentic coding / task automationClaude Code terminal-based agentic codingKilo Code VS Code extension for agentic, multi-step coding tasks; open-source fork of Cline with strong local model supportOpenCode terminal-native AI coding agent, model-agnostic; relevant for engineers who prefer CLI-first workflows or self-hosted setupsGitHub Copilot Agent modeDevin, 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 blindlyAI-assisted code review using LLM tools to pre-screen PRs, catch patterns, suggest refactorsTest generation with AI leveraging Copilot/Cursor to scaffold unit and integration testsContext 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 executionLLM & AI Platform FamiliarityOpenAI API / Azure OpenAIAnthropic API (Claude)AWS Bedrock or Google Vertex AI (for DevOps/cloud leads)LangChain or LlamaIndex basic understanding of RAG and chain patternsAI-Augmented DevOps / Platform (for DevOps leads specifically)AI-assisted IaC generation (Copilot + Terraform, Pulumi AI)GitHub Actions with AI steps or LLM-based pipeline stagesMonitoring/observability tools with AI anomaly detection (Datadog AI, AWS DevOps Guru)