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Tasks:
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.
What We're Looking For:
At least 5+ years of hands-on software engineering experience, including significant work on production-grade AI or data-intensive systems in complex delivery environments.
Excellent Python programming skills.
Proficiency in Azure and AWS cloud platforms.
Hands-on experience with Generative AI technologies and applications.
Strong practical understanding of LLMs, retrieval-augmented generation, tool use, agents, and their real-world trade-offs, limitations, and failure modes.
Availability to work between 2:00 PM and 10:00 PM CET is preferred.
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.
Adaptive mindset, high learning agility, and genuine openness to continuous change in a field shaped by rapid advances in AI.
Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field; equivalent experience considered for exceptional candidates.
