AI Engineer

Responsibilities:

• Build and continuously evolve shared AI capabilities, reusable services, and engineering accelerators that can be adopted across multiple construction software products
• Design and implement production-grade AI systems including LLM-based experiences, retrieval pipelines, tool use, orchestration layers, and agentic workflows
• Create human- and machine-executable technical artifacts such as specs, evals, interface definitions, and workflow contracts that improve clarity, speed, and quality of delivery
• Define and apply evaluation-driven development loops for AI systems, covering quality, reliability, latency, cost, grounding, and other relevant performance dimensions
• Partner with product teams to identify high-value cross-product opportunities, support integration of shared capabilities, and recommend patterns without owning product-specific architecture decisions
• Contribute to AI-native engineering practices inside the team through Spec-Driven Development, AI-assisted development workflows, automated testing, and intelligent engineering agents
• Apply practical standards and guardrails for AI reliability, observability, governance, security, and responsible production use of AI solutions delivered by the central team
• Prototype and validate new technical approaches quickly, turning emerging AI capabilities into scalable building blocks with clear business relevance
• Support and guide other engineers through technical collaboration, peer review, and knowledge sharing in modern AI engineering and experimentation
• Work closely with AI PM / PO, engineering, and business stakeholders to align implementation choices with customer value, delivery feasibility, and measurable outcomes

Requirements:

• 5+ years of hands-on software engineering experience, including significant work on production-grade AI or data-intensive systems in complex delivery environments
• Strong practical understanding of LLMs, retrieval-augmented generation, tool use, agents, and their real-world trade-offs, limitations, and failure modes
• Experience designing and operating end-to-end AI systems across APIs, application services, data pipelines, retrieval layers, evaluation workflows, and cloud infrastructure
• Ability to define measurable quality criteria and evaluation approaches for AI systems, balancing customer value with reliability, latency, cost, and maintainability
• Practical experience with AI-assisted engineering, Spec-Driven Development, and modern developer tooling; able to improve how teams work by combining human judgment with automation and agent support
• Strong architectural judgment with the ability to design reusable patterns and shared capabilities while collaborating effectively with product teams that own their local implementation decisions
• Demonstrated track record of contributing to cross-functional work from early exploration and prototyping through production rollout and continuous improvement in agile environments
• Ability to support and influence other engineers, raise technical standards through collaboration, and create clarity in ambiguous and fast-changing AI environments
• Strong communication and collaboration skills, with the ability to translate technical complexity into decisions and actions for product, engineering, and business stakeholders
• Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field; equivalent experience considered for exceptional candidates

Nice to have:

• Adaptive mindset, high learning agility, and genuine openness to continuous change in a field shaped by rapid advances in AI

Offer:

• Private medical care
• Co-financing for the sports card
• Life insurance
• Constant support of dedicated consultant
• Employee referral program