Type: Full-Time

Summary

The Site AI Engineer role operationalizes Suffolk’s AI strategy by embedding intelligent systems directly into construction workflows. It combines technical expertise, process design, and human-centered change management to ensure AI solutions deliver measurable value on active projects.

Focus

• Partner with each project AI Champion (PM/Superintendent) to identify pain points and redesign workflows
• Translate user stories into deployable AI agents and coordinate implementation with Suffolk AI Studio
• Advance “Construction Site of the Future” operations through measurable AI adoption

Responsibilities

• Lead Lean/Six Sigma-style discovery workshops and value-stream mapping
• Assess process and data maturity, identify adoption blockers, and define phased improvement plans
• Evaluate market and platform solutions, launch pilots, measure impact, and scale what works
• Build production-ready AI agents using Copilot Studio, Power Apps / Power Automate, and ChatGPT Enterprise

Led LLM/RAG architecture and advanced document processing for federal mission teams, including MCP-enabled spatial reasoning workflows across GIS platforms. Built evaluation harnesses, vector data systems, and production data pipelines with compliance-first delivery.

Delivered Python-based market analytics and GIS-driven site context analysis for real-estate decision support. Built workflows integrating public datasets (parcel, elevation, lidar, roads) to improve planning quality and operational execution.

Built and operated mission-critical scientific data systems, including CI/CD and cloud service workflows. Led delivery of research-enabling tools, enforced governance and testing standards, and improved reliability for high-volume production data operations.

Engineered large-scale data pipelines and analytics workflows on high-volume datasets, improving data accessibility and processing performance for product and operations stakeholders.

Developed large-scale location-data analytics and experimental data-science workflows across Spark and AWS. Drove feature engineering, quality improvement, and cross-team adoption of derived data products for targeting and reporting.

Delivered spatial data applications and database solutions for infrastructure projects, including PostGIS-backed analytics, implementation planning, and client-facing technical delivery across global teams.