Work authorizationUS citizen · full EU work authorization
About
I've been the first engineer in the door, the co-founder who set the architecture and the hiring bar, and the leader directing 50+ engineers across four business units through their managers. I've built platforms from the first commit and grown engineering teams from one person to fifteen, reaching thousands of customers as a co-founder.
These days my work is AI-native: multi-agent systems, RAG, tool and function calling, and MCP integrations, with the evaluation, guardrails, and governance that keep AI a force multiplier instead of a liability. I care about outcomes over activity, I stay close to the code, and I build the kind of engineering culture where strong people do their best work.
15+ years spanning early-stage builds and NASDAQ-scale organizations. I think like a product owner and stay technical enough to challenge a model, interrogate an incident, and keep the trust of the engineers I lead.
I've shipped under real compliance bars throughout my career: SOC 2, HIPAA/CLIA-accredited healthcare infrastructure with zero audit findings, and regulated financial and mortgage data at a NASDAQ company.
As a US citizen based in Barcelona with full EU work authorization, I can join teams on either side of the Atlantic with no sponsorship required.
What I do
Build and lead engineering teamsFrom first hire to a team of fifteen, and directing 50+ engineers through their managers across distributed, cross-continental teams.
Ship production AI, hands-onMulti-agent orchestration, RAG, tool/function calling, vector search, and MCP integrations; daily work with modern AI coding tools.
Scale platforms through growthHigh-volume, event-driven, near-real-time systems built from scratch and modernized without stopping the business.
Make AI adoption real and safeEnablement programs and responsible-AI governance (aligned to NIST AI RMF): guardrails, evaluation, least-privilege access, and human-in-the-loop review.
Lead with product and commercial judgmentUnit economics, roadmaps, and trade-offs translated into engineering strategy.
Co-founder and technical lead of an AI automation and fractional-engineering-leadership practice.
Build production agentic AI hands-on: multi-agent systems, RAG, and MCP integrations.
Built a real-estate ML decision engine (AppFolio/QuickBooks to BigQuery with a Looker layer) that scores hold, sell, refinance, and renovate per asset on DSCR, LTV, cap rate, and cash-on-cash return.
Set the standards, governance, and reference patterns for safe AI adoption.
Representative outcomes: cut a core billing process from three days to four hours; $250K+ in annual savings per engagement.
Built the full platform from the first commit on AWS, including a production ranking and lead-scoring engine serving 135M+ records at sub-second retrieval.
Reached 3,000+ clients and $50K MRR in 12 months; grew the team from 0 to 15.
After acquisition, led the technical integration into a live multi-product platform with no customer interruption.
Ran a cross-continental engineering team (US, India, Europe) through their leaders.
First engineering hire; grew the team from 1 to 15.
Built a high-volume, event-driven streaming platform on AWS (Kafka, Spark on EMR, ECS) with proprietary data-quality, fraud-detection, and bot-detection algorithms.
Shipped a self-serve delivery portal that cut client onboarding from 3+ months to under 24 hours.
Manager → Senior Manager, Product Development · Altisource (NASDAQ: ASPS)
2014 – 2020 · Denver, CO + Global · started at RentRange, acquired by Altisource in October 2015
Started at RentRange in 2014 and grew through its acquisition by Altisource in October 2015 into enterprise engineering leadership.
Directed 50+ engineers across four business units, through directors and managers, on regulated financial and mortgage data.
Led enterprise cloud modernization to AWS with multi-million-dollar cost reductions.
Led roughly 7 M&A integrations.
Led a patented Rental Automated Valuation Model and an automated buy-box, property-matching, and underwriting platform that sourced, valued, and acquired single-family homes for investors at scale.
Real-estate investors and operators decide whether to hold, sell, refinance, or renovate each property using data scattered across property-management and accounting systems, so the analysis is slow, manual, and inconsistent from asset to asset.
Approach
Built a pipeline that pulls from AppFolio and QuickBooks into BigQuery, computes per-asset financials, and scores each property's best next move, surfaced through a Looker layer the team can act on.
A core billing process was manual and multi-step, taking about three days of human effort each cycle and leaving room for error.
Approach
Built a multi-agent system (RAG and MCP integrations) that runs the billing workflow end to end, with human-in-the-loop checks at the points that matter.
Teams adopting AI tools move fast but without shared guardrails, risking quality, security, and inconsistent results as adoption scales.
Approach
Established reference patterns, guardrails, and enablement programs aligned to NIST AI RMF: evaluation in the workflow, least-privilege access for agents, and human-in-the-loop review.
Every parcel leaving a warehouse was a missed marketing opportunity: e-commerce brands had no way to turn the physical shipping label into an owned, addressable channel.
Approach
Co-founded the venture and led engineering as CTO, building the full stack (a Shopify app for brand onboarding and campaigns, a Chrome extension, a ShipStation desktop integration for 3PL hubs, and a NextJS/React advertiser dashboard) on patented QR-driven label technology, and drove the live pilot integrations with ShipStation and USPS.
A prospecting platform is only as good as its search: users have to find and rank the right B2B contacts out of hundreds of millions of records, instantly.
Approach
Built the production ranking and lead-scoring engine from the first commit on AWS, tuned for sub-second retrieval across the full dataset so prospect search felt instant.
After the acquisition, a live product with paying customers had to be merged into the acquirer's multi-product platform without downtime or disruption.
Approach
Led the integration incrementally so customers kept working throughout, coordinating a cross-continental team (US, India, Europe) through their leaders.
Location data at scale is noisy: fraud, bot traffic, and low-quality signals degrade the product if they reach customers.
Approach
Built proprietary data-quality, fraud-detection, and bot-detection algorithms directly into the streaming pipeline, catching bad data upstream rather than after delivery.
Regulated financial and mortgage data platforms across four business units ran on aging infrastructure that was costly and hard to scale.
Approach
Led the modernization and re-platforming onto AWS across the business units, keeping delivery running throughout, with a cloud-first template that scaled org-wide.
Institutional investors buying single-family homes need to source, value, and underwrite properties against a defined buy-box far faster and more consistently than manual analysis allows.
Approach
Led Investability, an automated buy-box, property-matching, deal-scoring, and underwriting platform that sourced, valued, and acquired single-family homes end to end.
A NASDAQ company growing by acquisition has to fold acquired companies' technology and teams into the enterprise platform without breaking either side.
Approach
Led the technical side of roughly seven M&A integrations: due diligence, integration, and separation, bringing acquired systems and distributed teams onto the platform.
As a player-coach. I set the technical direction and the standards, then stay close enough to the work to review the hard implementations, unblock people, and step into a production problem when it helps. I lead through managers as a team grows, developing leaders rather than routing everything through me, but I never drift so far from the code that I lose the judgment to tell good technical decisions from expensive ones.
How do you actually use AI with a team?
Every day, and deliberately. I build production agentic systems myself (multi-agent orchestration, RAG, tool calling, MCP), so I know where AI earns its keep and where a deterministic workflow is the better call. With a team, adoption is a people problem more than a tooling one: I find a champion, pair, train, and measure against cycle time and quality rather than lines of code. And I put the guardrails in first (evaluation, review discipline, least-privilege access, human-in-the-loop) so AI stays a multiplier instead of a cleanup generator.
How do you hire and grow engineers?
I set a clear bar and protect it, then invest hard in the people I bring in. I've grown teams from one engineer to fifteen and directed 50+ through their managers, so I've learned to develop managers and senior leaders, not just individual contributors. I give direct, specific feedback, I create room for people to take on things slightly beyond their current level, and I treat retention as a design problem: reducing knowledge silos so a team stays resilient through growth and change.
How do you modernize a platform without stopping the business?
Incrementally, and with a visible plan. I've led cloud modernizations, legacy consolidations, and acquired-product integrations where the business had to keep running the whole time. I map the technical debt, sequence the work so each step ships value and nothing goes dark, and I'm honest about when to extend, when to refactor, and when to replace. The goal is to make the platform stronger as it scales without ever handing customers a disruption.
You've been a founder. Why take a role instead of starting another company?
Because the work I most enjoy is building the product and the team, and I can do that inside one company I believe in without needing to own the cap table to feel ownership. As a founder I sat at the leadership table and carried outcomes and unit economics directly, so I bring that operator's mindset to a role. I just get to point all of it at one mission instead of running the whole business at once.
How do you work as a remote, distributed leader?
It's how I've worked for years: teams across the US, India, and Europe, led through their leaders. Remote-first rewards clarity: good written communication, lightweight process for planning and incidents, and trust with accountability rather than presence as a proxy for progress. I'm a US citizen based in Barcelona with full EU work authorization, so I can join teams on either side of the Atlantic without sponsorship.
What are you looking for in your next role?
A real leadership seat where I can be hands-on, at a company where AI is genuinely part of how the product and the team get built. I do my best work in regulated or operationally complex domains (fintech, proptech, healthcare-adjacent, data platforms) and at the stage where growth is stretching how the team works and someone has to build the operating rhythm for what's next. I care most about the mission, the people, and getting to do the work, not the title.
Why do you stay hands-on as a leader?
Because it's how I keep honest judgment and credibility. Building things myself is how I can tell the difference between AI that's buying real leverage and AI that's quietly creating a cleanup job later, how I stay current on the thing that's changing fastest, and how I keep the trust of the engineers I lead. A leader who's drifted away from the work loses the ability to make the calls that matter most.
