AEC Domain Architect (Agentic AI)

Do you have experience in SQL?, Do you have a Master's degree?, * Top-Tier Pedigree: MS or PhD in Civil Engineering, Construction Management or a related field from a Tier-1 institution

• Data Science Literacy: Strong background in data science (Python, SQL, statistical modeling) and understanding of how ML models are trained and validated
• Domain Context Engineering: Ability to structure domain knowledge into ontologies for AI training
• Modern Delivery Frameworks: Strong practical knowledge of Lean Construction, Target Value Delivery (TVD) and Integrated Project Delivery (IPD)
• Communication: Ability to fluently translate complex engineering and construction workflows to ML and product engineers

CONXAI has built a no-code, agentic AI platform for the AEC and physical industries, focused on knowledge-automation. We codify high-stakes, knowledge-intensive workflows traditionally trapped in siloed data, fragmented tools and tacit (undocumented) human expertise. Our multi-agent systems perform complex reasoning in the physical world; and transform bespoke, service-heavy processes into scalable Service-as-a-Software automation. CONXAI is trusted by some of the leading AEC companies in Europe, US, LATAM and Japan. Your Role: Encode the DNA of Construction into our AI We are looking for a rare hybrid: a Civil Engineering expert who speaks the language of Data Science. You will define how AI understands construction concepts, logic, workflows and site dynamics. You will sit at the intersection of Product, AI Engineering and the Jobsites, ensuring our AI models deliver high-fidelity value to automate complex processes. * Domain Modeling: Translate AEC workflow and methodologies (Lean, TVD, IPD) into logical architecture and ontology for agentic AI * Framework Implementation: Build out the platform's Domain Knowledge Base, to ensure that the AI understands BIM-to-Field relationships and productivity metrics * Data Strategy: Work alongside our ML team to define and structure high-quality AEC dataset (visual, sensor, and textual) to train agentic models * Product-Ownership: Represent the "voice of customer". Prioritize the product backlog based on first-principles understanding of construction pain points * Validation: Test AI outputs against industry standards to ensure it delivers practical value in real-world construction scenarios, Why CONXAI * Edge of Innovation: Be at the absolute forefront of AI in the construction tech space * High Autonomy: Own the domain strategy and shape a product that directly impacts how global infrastructure is built * Top-Tier Peer Group: Work with a global team of ML engineers, software engineers and industry practitioners * Equity & Scale: Competitive compensation with significant equity upside