Engineering leadership for AI-enabled products from 0→1 through scale.
Engineering Manager with 7 years building and scaling AI-enabled products across real estate and automotive. Leads teams across backend, infrastructure, and data/ML while staying close to architecture, delivery quality, and business outcomes.
Management scope backed by hands-on engineering judgment.
The differentiator is not just leading teams. It is being able to connect product direction, execution cadence, architecture, AI capability, and cost discipline in one operating model.
Delivered Computer Vision, LLM, and ranking capabilities that increased meaningful actions in 2025.
Reduced cloud and third-party API spend through caching, vendor alternatives, and performance tuning.
Built OKR planning, ownership, and weekly delivery cadence across launches, monetization, and iteration.
Doubled data-cleaning throughput by automating repetitive data collection and listing cleanup work.
Career progression across management, AI, and delivery execution.
Engineering Manager - Backend Lead
Led a 12-engineer team across CarNow and HouseNow, combining architecture ownership, product execution, analytics instrumentation, and monetization delivery.
- Saved $50K+ in 2025 through architecture, cloud, and API spend decisions.
- Supported 50,000+ MAU at peak while improving reliability and delivery predictability.
Engineering Manager - AI Engineer
Managed a 6-engineer team researching and building an alternative renting solution for the Boston market.
- Improved conversion rate by 20% through a recommendation system.
- Balanced product research with applied AI delivery.
Project Manager - Computer Vision Engineer
Worked directly with Japanese clients on computer vision delivery, project planning, and cross-functional execution.
- Managed a 5-engineer team delivering a solar panel maintenance solution.
- Previously built OCR products as an AI engineer in a 4-person team.
How the work gets led.
Builds OKR cadence, sprint rituals, and ownership clarity so engineering work stays tied to business priorities.
Stays close enough to backend, infrastructure, data, and AI systems to evaluate risk, architecture, and tradeoffs.
Treats reliability, infrastructure efficiency, and engineering standards as management responsibilities, not afterthoughts.
Broad enough for portfolio conversations, curated enough to stay readable.
- Project and product management
- Workflow optimization
- Data analysis
- Systems architecture
- DevOps and CI/CD
- Infrastructure budget optimization
- Product R&D
- Recommendation systems
- Computer vision and LLM-enabled workflows
- PostgreSQL, MySQL, MongoDB
- GCP, AWS, Terraform, GitHub Actions
- Metabase, BigQuery, PostHog
If the background fits, the resume is one click away.
The site is intentionally concise. The PDF resume carries the full history, while the contact page keeps direct channels easy to find.