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The person we are looking for will become part of Data Science and AI Competency Center working in AI Engineering team.
What you'll be doing
- 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.