Software Engineer - AI

About this role

ABOUT THE ROLE This is a full-stack AI engineering role on a small, technical product and engineering team building AI-powered tools for construction professionals. You will own features end-to-end, from database schema and LLM integration through to UI, while also contributing to the internal platform foundations that let new use cases ship quickly. WHAT YOU'LL DO - Build and own end-to-end AI features from concept through production, including document versioning and agentic chat workflows.

- Design and implement features across the full development lifecycle: database schema, LLM tuning, evaluation, and UI. - Manage evaluations, scoring, and error analysis on new and shipped AI features to ensure quality. - Contribute to the workflow harness, decision-trace standard, and evaluation tooling that accelerates shipping new use cases. - Build reusable human review and approval surfaces that earn trust in AI output.

- Model and evolve a construction domain ontology and project graph, including entity resolution across documents such as bills of quantities, contracts, and drawings. - Design features that link answers to evidence and make decisions auditable and traceable. - Extend multi-modal ingestion of unstructured documents (PDFs, scans, drawings, and 3D models) at scale. - Build read/write integrations with customer systems (document management, ERP) to complete workflows end-to-end.

- Contribute to scalable data pipelines and support native BIM/DWG file processing. WHAT WE'RE LOOKING FOR - 3+ years of experience as a full-stack software engineer, ideally in fast-paced, early-stage B2B startup environments. - Proven track record delivering production-grade applied AI features to customers. - Proficiency with TypeScript, Python, and SQL for building full-stack applications. - Experience building backend APIs and database schemas using frameworks such as Fastify and Prisma, or equivalents.

- Experience integrating and optimizing LLM APIs (OpenAI, Azure, Gemini, Cohere, or similar). - Experience with cloud infrastructure such as AWS (RDS, S3, Elastic Beanstalk) or equivalent. - Experience building data pipelines and ETL workflows for processing unstructured data at scale. - Experience with vector databases and semantic search implementations. - Experience designing and implementing evaluation frameworks and error analysis for AI systems.