Featured in Forbes

Software engineer & engineering leader

I build the software and lead the teams that ship it.

Hands-on across full-stack product development, AI systems, APIs, performance, and delivery. I bring 20+ years of engineering experience and the judgment to turn ambiguous product needs into reliable customer-facing software.

Stephen Weber
20+ years building software
2 Acquired Products
45% faster page loads
4.0s → 788ms query latency
500+ users onboarded

What I bring

Technical depth, product judgment, and accountable delivery.

I am strongest where technical ambiguity and customer urgency meet: turning a rough product need into an architecture, a working system, and a release the team can support with confidence.

Engineering & architecture

Full-stack applications, AI and LLM systems, APIs, data workflows, performance work, and production troubleshooting. I make pragmatic tradeoffs around reliability, observability, security, cost, and user experience.

TypeScript · JavaScript · Python · React · Next.js · Node.js · SQL · GraphQL · Redis · Databricks

Leadership & delivery

Hiring, coaching, technical direction, quality practices, release discipline, and product partnership. I help teams move faster by making scope, ownership, and engineering standards clear.

AI systems · RAG · Agents · MCP · AWS · GCP · CI/CD · Test automation · Observability

Selected experience

Technical ownership at product, platform, and team scale.

I have held engineering, management, and founder roles. The common thread is shipping useful software where product urgency, system quality, and delivery accountability meet.

More on how I lead and deliver
  1. Nov 2023 – Nov 2026

    Blackbird.AI

    Principal Software Engineer → Senior Engineering Manager → Director of Engineering

    Led App Platform engineering and QA while continuing to build production AI capabilities. Improved Narrative Page loads by up to 45% and reduced a difficult SQL query from 4 seconds to 788 milliseconds through API batching, Redis caching, Databricks investigation, and monitoring.

  2. 2022 – 2023

    You.com & Dispatch

    Engineering Manager

    Led teams delivering AI search and Web3 messaging products, pairing practical architecture and hands-on engineering work with planning, quality, release discipline, and cross-functional delivery.

  3. 2015 – 2022

    Experian

    Manager of Innovation

    Built fraud detection, credit-risk, API, mobile, and cloud-native product initiatives across data platforms, machine learning, and customer-facing software.

  4. 2006 – 2017

    Weber LLC

    Founder & product engineer

    Delivered 40+ web and mobile applications across SaaS, ecommerce, media, AI-assisted consumer products, and rapid-response utility tools.

How I work

Principles for building software that holds up in production.

Engineering Operating Systems

I make ownership, planning, quality, and delivery visible enough that teams can move quickly without relying on heroics.

Clear operating rhythms, better scoping, automated checks, and accountable handoffs help teams scale output before adding headcount.

Production AI Systems

I treat AI as software that has to be reliable, observable, secure, and economically defensible.

The work is not just model selection. It is evaluation, workflow design, cost control, monitoring, security review, and human-centered product judgment.

Product-Engineering Partnership

I connect technical decisions to customer urgency, revenue, adoption, and trust.

Strong product partnership keeps architecture, roadmap tradeoffs, security constraints, and delivery pressure pointed at the same business outcome.

Reliability, Security, and Delivery

I push for speed with discipline: stronger quality gates, tighter release habits, and pragmatic security judgment.

Teams earn speed by reducing ambiguity, catching defects earlier, and making customer-impacting risk explicit before it reaches production.

Technical writing

Notes on engineering systems that have to work beyond the demo.

Production AI

Design Patterns for AI-Powered Applications

The gap between impressive AI demos and reliable production systems has never been wider. Engineering discipline — not model choice — determines outcomes.

Technical Deep Dives

Mastering LLM Accuracy: How to Test, Detect, and Fix Hallucinations

A practical guide to testing, detecting, and reducing hallucinations when AI systems need to be trusted in production.

Engineering Strategy

Choosing the Right Architecture for Scalable Generative AI Apps

A breakdown of the best architecture patterns for building generative AI applications that actually scale beyond the demo stage.

Selected work

Products built from the problem outward.

AI products, developer tooling, and SaaS experiments shaped by specific customer and engineering constraints.

Acquired
BitRook

BitRook

Machine Learning

An ML-powered platform that profiles your data, flags issues, and recommends fixes—without writing code.

Problem
Data quality issues slowed ML teams and created downstream model failures.
My role
Founder and product-engineering lead from concept through acquisition.
Engineering contribution
Built the product from concept through acquisition, including its data workflows, connector architecture, and ML-assisted recommendations.
Outcome
Acquired by an enterprise data company after building the product, team, and integration strategy.
PythonDeep LearningTensorflow
View case study
Acquired
Voteboards

Voteboards

B2B SaaS

A simple app that lets users create boards to collect and upvote ideas—perfect for product roadmaps.

Problem
Product teams needed a lightweight way to collect, prioritize, and act on customer feedback.
My role
Founder and builder responsible for product direction, full-stack delivery, and sale process.
Engineering contribution
Owned product direction and full-stack delivery for a focused SaaS workflow.
Outcome
Acquired by a productivity software company.
ReactNode.jsProductivity
Visit website
Open Source
Founder Flow

Founder Flow

AI Agents

Input your product idea—Founder Flow takes it from there. A full-stack autonomous startup builder.

Problem
Founders lose time on repetitive pre-launch work before validating whether an idea deserves investment.
My role
Creator of an open-source AI agent system for startup asset generation.
Engineering contribution
Designed and built the agent workflow, modular pipeline, and feedback loops for repeatable startup-asset generation.
Outcome
Demonstrated how AI workflows can compress idea-to-prototype cycles while preserving structured product thinking.
LangflowLLMsAutomation
View case study