Applied AI Product Engineer
Product engineer · Search, RAG, recommendations, learning systems
I build AI features end to end and ship them to production.
I’m a software engineer with 12+ years across full-stack product engineering, platform work, and applied AI. Currently building AI systems for medical education: semantic search, RAG, recommendations, and personalized study plans for clinicians.
Washington-based · Hawaii/HST-friendly · U.S. remote
You're inside an embedding space, the kind of system I build. Scroll to travel through it.
Work I'm drawn to
I like building product features that use AI where it actually helps: shipping the first useful version, integrating it with real systems and data, measuring whether it works, and turning repeated needs into reusable product capabilities.
This overlaps with work often called AI product engineering, applied AI engineering, or product engineering for AI systems.
Retrieval-augmented search
Semantic search across a clinical learning library
Built retrieval-augmented semantic search to help clinicians find relevant learning content by meaning, not just keywords. Work included embeddings, pgvector/Postgres, retrieval strategy, product integration, and production rollout.
- RAG
- OpenAI embeddings
- pgvector
- Postgres
- Search UX
Recent applied-AI work for medical education, used in production by thousands of clinicians.
Personalized recommendations
Behavior-driven recommendations
Built personalized recommendations that adapt to member behavior and content relationships. Designed the recommendation logic, data model, retrieval strategy, and full-stack product integration.
- Recommendations
- Personalization
- Product engineering
- Content discovery
Assessment-driven personalization
Assessment-to-study-plan pipeline
Turned group-level assessment misses into targeted study recommendations. Connected assessment data, content mapping, retrieval, reranking, and product UX to help teams identify and close knowledge gaps.
- Data pipelines
- Reranking
- RAG
- Learning systems
In progress In progress
Personalized retention engine
Building a daily learning plan that combines spaced repetition, recommendations, and retrieval to help clinicians retain important material over time.
- Spaced repetition
- Retention
- Personalization
- Applied AI
How I work
I’m comfortable starting with unclear requirements, legacy constraints, partial data, and real users who do not care about AI for its own sake. The work I enjoy most is turning that ambiguity into useful, reliable product behavior.
- Frame Understand user pain, constraints, and success criteria.
- Build Prototype quickly, then turn the useful parts into production-grade systems.
- Integrate Connect AI features to existing data models, APIs, auth, and product surfaces.
- Measure Evaluate relevance, adoption, latency, cost, reliability, and user trust.
- Productize Turn repeated needs into reusable product capabilities instead of one-off hacks.
Tools I reach for
AI / retrieval
OpenAI, embeddings, RAG, reranking, pgvector, vector search, LangChain, evaluation patterns
Product engineering
TypeScript, React, Node.js, Postgres, GraphQL/REST, full-stack product architecture
Production reality
APIs, data modeling, observability, auth, performance, cost/latency tradeoffs, production debugging
Also shipped
Node.js, Ruby on Rails, React Native/Expo, native Android and iOS; earlier work in Go, Python, and Java
2023 — Present
Hippo Education
Senior Software Engineer, Platform / Applied AI
Designing and shipping net-new AI product features for medical education, including semantic search, RAG, recommendations, assessment-based study plans, and personalized retention tooling for clinicians. Also re-platformed mobile from bare React Native to Expo/EAS and migrated the backend from Firebase to GraphQL/Postgres.
2021 — 2022
Made Renovation
Engineering Manager & Senior Software Engineer
Managed a team of 5 engineers on core services — sales, fulfillment, invoicing, and project timelines. Built the CI/CD and red/green deployment process, and migrated a legacy system managing 1k+ renovations to a new integrated sales + fulfillment platform.
2018 — 2021
Varsity Tutors
Software Engineer, Sales Ops
Built checkout, payment, enrollment, and internal sales tools for a high-throughput education marketplace.
Let's talk
Building AI features into a real product?
p@patrickeddy.comWashington-based · Hawaii/HST-friendly · U.S. remote
© 2026 Patrick Eddy