For Principal Engineer Jay, the journey from building data infrastructure to creating autonomous AI agents for the construction industry has been a rewarding, five-year evolution at Procore. Jay, who now works on the Applied AI team within Procore’s AI & Data division, is realizing a long-held professional aspiration: applying the power of artificial intelligence to solve complex, high-value problems in an industry where digital transformation is not optional, but essential.
From Data Foundation to AI Agents
Jay’s role is central to Procore’s mission to establish its AI as the definitive intelligence layer for construction. As he reflects, "Big data transformed the industry over a decade ago, and AI is now reshaping it at an even faster pace. We’ve got decades of real construction data pulled from over three million customer projects as our foundation, and now we're building intelligent systems on top of it that can reason, remember, and act."
When Jay joined Procore in September 2020, the company was growing fast and still retained the creative atmosphere of a startup. Initially, he focused on strengthening Procore's data platform, a crucial component that directly impacts customers' ability to make strategic decisions. One of his proudest moments was a massive team undertaking to significantly reduce data latency, ensuring customers could access real-time, reportable insights. This work laid the essential groundwork for what came next.
Driving the Autonomous Future with Datagrid
One of the most exciting chapters in Jay's career—and for Procore itself—was the acquisition of Datagrid and the formation of the AI & Data division. His mission was clear: to help turn the Procore platform into an autonomous system that drives productivity, profitability, and project success. Datagrid’s core value is connecting fragmented data sources such as ERPs, cloud storage, and document repositories, then applying AI reasoning to orchestrate actions across them.
Jay is now on the front lines of this transformation, helping build the agentic platform that enables customers to create AI agents that can understand context, reason through complexity, and take meaningful action across real-world workflows. These AI agents help construction companies automate their most complex tasks, putting the data infrastructure he helped build years ago to work in sophisticated new ways.
This focus on the intersection of data and AI is exactly what Jay has been building toward his entire career. “I’ve been in the data space since 2017, and the AI work I dreamed about back then is now my reality. On the Applied AI team, I’m building the reasoning and memory systems behind Datagrid’s agents–turning data into intelligent systems that can think, remember, and act on behalf of construction teams.”
The impact is tangible: the AI capabilities Jay's team builds are saving real people time and money, enabling them to get their work done safely and more efficiently.
The Challenges of Construction and AI
Construction presents unique problems for AI, such as spatial reasoning, optical character recognition (OCR) on messy blueprints, understanding construction terminology and semantics, and the challenge of low-latency, high-reliability requirements on job sites.
A key element of Jay’s work is helping to scale the agentic platform to meet the enterprise demands for performance, reliability, and cost efficiency. In practice, that means solving the hard engineering problems that determine whether AI can move beyond promising prototypes and become something customers can depend on every day. For Jay, that challenge is especially exciting because it sits at the intersection of infrastructure, data, and applied AI.
A Passion for Growth and Culture
Beyond his technical contributions, Jay is a firm believer in Procore’s culture, which is defined by our people and shared vision. “Hands down, the people and culture–especially my team and the folks I work closely with–make Procore a great place to work.” He appreciates the willingness of his colleagues to help, talk through challenging work, and share their diverse expertise. Jay is also thankful for the opportunity to mentor junior engineers, providing high-level context before tailoring his guidance to their preferred learning style, ultimately giving them the driver’s seat to grow in their careers.
For Jay, Procore has been the kind of company he hoped to build his career in–a company that is building something from scratch and making a genuine impact on how the physical world is built.
Q&A With AI Engineer Jay
Jay shares more insights on his career path, the technologies he uses in his AI work at Procore, critical skills to succeed in AI engineering, key technologies driving Procore AI, and his advice into getting into the field.
Q: How did your career lead you to work in AI?
A: In 2006, my journey into the software world began when I took a job as a software engineer to do backend project management work. It’s a vast field, so I made it my mission to expose myself to as many areas as possible. When the iPhone launched in 2007, I joined a mobile distribution digital asset management company on their mobile management solution. When the mobile world exploded a few years later, I helped a new company build its mobile web solution.
In 2012, that company was acquired by a U.S.-based business, which brought me to Austin, Texas. From there, I worked in cloud computing before joining Expedia’s big data team, where I spent years deepening my experience in data platforms and large-scale systems.
By that point, I had been in the data space for close to a decade, and I started thinking carefully about where I wanted to focus the next stage of my career. AI was the field I was most excited about, but I also saw that strong data foundations were essential to making AI work well in practice.
That realization led me to Procore in 2020. I started by working on the data platform, and over time, that foundation naturally evolved into AI-focused work. In many ways, it connected the two areas I had been building toward for years: data and intelligent systems.
Q: What technologies do you use in your work as a Principal Engineer on an AI team?
A: I work across a mix of data, infrastructure, and AI technologies. That includes large language models, retrieval systems, distributed data platforms, and the orchestration frameworks needed to turn those pieces into reliable production systems. A big part of the job is not just using AI models, but building the platform capabilities around performance, cost efficiency, and reliability so they can operate at enterprise scale.
Q: What are the critical skills you think are important in AI-focused engineering roles?
A: Strong engineering fundamentals still matter the most. You need to understand systems design, data, performance, and how to build software that is reliable in production. On top of that, AI engineers need to be comfortable working through ambiguity, learning quickly, and balancing tradeoffs like quality, latency, and cost. Curiosity is also important because the field moves fast, and the best engineers are the ones who keep adapting.
Q: What are the key technologies that are driving Procore AI?
A: The three key technologies that are driving Procore AI are:
- The Multi-Control Plane will enable the platform to move from simple automation to autonomous action, allowing AI agents to "reason" and act intelligently across various tasks without constant human oversight.
- A Multi-Agent System (MAS) that allows Procore AI to tackle multi-step, sophisticated processes—like drafting an RFI from fragmented project data—by breaking the problem down and assigning the sub-tasks to the most qualified agent.
Vector Databases. These are a crucial component of modern AI architectures, especially those involving Large Language - Models (LLMs) and Retrieval-Augmented Generation (RAG). This ensures Procore's AI agents provide accurate, context-aware, and up-to-date responses based on the customer's specific, proprietary data, drastically improving the reliability and usefulness of the AI in a construction context.
Q: What advice would you give to someone aspiring to work in AI engineering?
A: Build strong fundamentals first, especially in software engineering, data, and systems thinking. Then spend time actually building things, because that’s where you learn the gap between what looks exciting in theory and what works in production. A lot of the real learning happens when something doesn’t work, because that’s when you’re forced to understand the system more deeply. AI is moving fast, so curiosity, adaptability, and a willingness to keep learning are just as important as technical skill. Our Data & AI teams are growing; if this the kind of work you are interested in, keep an eye on our job postings.
About the Author:
Melissa Heidmiller has been amplifying the voices of Procorians since 2021 to help attract talent to build meaningful careers at Procore. Melissa leads Talent Brand and Employee Advocacy, and has been able to draw on her experiences in career development, where she has guided hundreds of individuals on making positive career choices, blended with her work in communications and branding, where she strengthened her skills in developing narratives that attract desired audiences. Melissa holds a BA in Communications from Wilfrid Laurier University and a Career Development Practitioner diploma from Conestoga College. Melissa has shared her passion and interest in career navigation and employer branding as a speaker for DisruptHR and as a guest on several podcasts.
- AI
- AI Engineers
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