NY AI Engineers: Sept Tech Talk w/ Arthur, Browser Company & Graphite Presenting

New York AI Engineers is a community for anyone passionate about the craft, impact, and future of engineering. Whether you’re a fullstack developer exploring LLMs, a data scientist testing new tools, or an AI engineer shipping production systems, you’ll find your people here.

Our mission is simple: build a space where engineers can share projects, ask questions, learn from each other, and form lasting connections across the NYC tech scene.

We make this real through a series of tech talks designed to spark honest conversations and meaningful relationships.

September AI Engineers Meetup Recap

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We brought together three of NYC's top ML engineers and technical leaders for an evening of deep technical discussions about building real-world AI products. This wasn't another surface-level AI hype session, it was engineers in the trenches sharing what's actually working, what's breaking, and the hard-earned lessons from shipping AI features to production.

Featured Speakers

Zach Fry – VP of Engineering @ Arthur

Zach leads engineering at Arthur, a platform focused on AI observability and model monitoring. He shared insights on the operational challenges of keeping ML models reliable at scale, the evolving landscape of LLM monitoring, and how teams are adapting their infrastructure to handle the unique demands of generative AI in production environments.

Greg Foster – Cofounder & CTO @ Graphite

As CTO of Graphite, a tool reimagining code review workflows, Greg dove into how they're integrating AI to make developer tooling smarter and faster. He discussed the technical architecture decisions behind building AI-native developer tools, the balance between automation and developer control, and real-world learnings from deploying LLMs in high-stakes engineering workflows.

Jonathan Lopez – Prompt Engineering Manager @ The Browser Company

Jonathan manages prompt engineering at The Browser Company, makers of Arc browser. He pulled back the curtain on what prompt engineering actually looks like at a product company. Moving beyond tweets and tutorials to talk about evaluation frameworks, iteration cycles, managing prompt complexity at scale, and how his team ensures consistent AI behavior across millions of user interactions.

What Made This Special

This wasn't a pitch fest or a demo day. Each speaker brought real technical depth. Sharing architecture diagrams, code patterns, failure stories, and the messy middle of building with AI that rarely makes it into blog posts. The audience asked hard questions about latency, cost optimization, model selection, evaluation metrics, and production debugging strategies.