Senior AI Engineer - Agentic Systems

Senior Agentic AI Engineer

Join a dynamic and fast‑evolving team that is building next‑generation AI‑based tools and agent systems for the construction industry. Our AI and Data Team focuses on designing intelligent AI agents and copilots using modern AI/ML techniques.

• Build end‑to‑end GenAI solutions—develop, refine, and implement advanced GenAI models and ensure successful delivery of projects.
• Develop agents over our construction data estate that answer non‑trivial questions, take multi‑step action against APIs and databases, and operate under governance constraints that matter.
• Design tool‑use and orchestration in Lang Graph: define the right granularity of tools, state machines, and human‑in‑the‑loop checkpoints for a domain where wrong answers have real‑world consequences.
• Build evaluation infrastructure for non‑deterministic systems: harnesses, golden datasets, and regression tests that allow us to ship agentic features with confidence.
• Build retrieval and knowledge architecture spanning Snowflake Cortex, vector search, and structured graphs over our project data.
• Integrate with domain systems: partner with engineers and analysts on safety, operations, scheduling, and risk to turn agentic capabilities into tools superintendents and PMs use.
• Provide technical direction across the Agentic AI track: design reviews, architectural guidance, raising the bar on what “production‑ready” means for agents, and mentoring engineers.
• Collaborate with stakeholders, presenting findings to a non‑technical audience and providing strategic recommendations.
• Ensure scalability, reliability, and security of AI solutions by implementing best practices for model development, deployment, and maintenance.

• 6+ years of production software engineering, with at least 2 years building LLM‑powered systems in production.
• Demonstrated experience designing and shipping agentic systems using Lang Chain and Lang Graph or comparable frameworks.
• Strong Python engineering fundamentals: testing, packaging, performance, and the parts of the stack that aren’t glamorous.
• Practical experience with retrieval architectures (vector stores, hybrid search, re‑ranking) and at least one major cloud data platform.
• Track record of evaluation work; able to describe specific eval systems built and what they caught that ad‑hoc testing missed.
• Excellent written and verbal communication, with experience presenting technical work to non‑technical stakeholders.

• Snowflake and Snowflake Cortex (Cortex Search, , Cortex Analyst).
• Experience with knowledge graphs or graph‑augmented retrieval.
• Familiarity with construction, AEC, or other physical‑industry domains.
• Experience working under AI governance frameworks like model risk, responsible AI, intake processes.
• Open‑source contributions to the Lang Chain/Lang Graph ecosystem or related agentic tooling.

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