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Principal product and engineering leadership across mainnet launch, token airdrop, AI developer experience, and governance transition.
BlockchainAI DXPython
Contact
The future of the contractor, the agency, and the firm.
What used to require an agency, a consultancy, or a dedicated team now runs through one senior operator and purpose-built AI tooling. No bench. One accountable person.
Denver, CO
Best fit
Product or platform launches, turnarounds, and exit preparation that need range and speed, not a bigger team.
AI transformation that needs to be built and operated, not just strategized.
Ventures and teams that need full-lifecycle ownership from one seat instead of a larger, slower structure.
Simon J Hill
LinkedIn4 exits: Rachio/Rain Bird, if(we)/ParshipMeet Group, AOL/Verizon, Spire Digital/Valtech
Previously VP Product at Rachio, Director at Meet Group, Principal PM at AOL/MapQuest
25 years across product, engineering, and operations
Built and managed product, design, ML/AI, and protocol engineering teams at scale
Former management consultant to Chevron, U.S. Department of Energy, URS, and the United Nations
Oxford (MA Linguistics, BA Oriental Studies), UCL (MA Philosophy)
Built and operated large-scale behavioral products across social, dating, gaming, IoT, and AI.
40M+
Daily users at peak
$75M+
Revenue line managed
4
Exits
25
Years shipping
3
Patents
Capabilities
Everything the firm would staff. One operator.
Product leadership
Strategy, opportunity framing, roadmap design, pricing, sequencing, and go-to-market decisions that keep product and business logic tied together.
Includes
Discovery, positioning, prioritization, launch planning, monetization
Design and growth
User journeys, interface design, conversion systems, lifecycle loops, and the measurement layer needed to improve them without guesswork.
Includes
UX, UI, design systems, analytics, SEO, lifecycle, experimentation
Application delivery
Hands-on implementation across web, mobile, backend, and data plumbing, with a bias toward maintainable systems rather than heroic prototypes.
Includes
React, Vue, Next.js, Python, Django, FastAPI, PostgreSQL, Redis
AI systems
Practical uses of LLMs where they improve throughput, decision support, workflows, or product experience without turning the product into a gimmick.
Includes
OpenAI, Anthropic, RAG, embeddings, orchestration, internal tools
Data and instrumentation
Event models, reporting, operational dashboards, forecasting, and decision support for teams that need fewer opinions and better visibility.
Includes
Snowflake, BigQuery, dbt, Metabase, Looker, Tableau, Streamlit
Infrastructure and specialist domains
A working grasp of the platform layer, plus access to trusted specialists when the job genuinely needs deep security, DevOps, or domain-specific reinforcement.
Includes
Docker, Kubernetes, Terraform, Cloudflare, observability, payments, Web3
Typical engagement model
Keep the system simple enough to move.
01
Start with the constraint that matters most: growth, delivery speed, monetization, AI adoption, or product reset.
02
Restructure execution and operations for maximum AI leverage without losing accountability.
03
Instrument the work so decisions improve with evidence, not opinion.
Portfolio
Selected products to demonstrate scope and depth.
12 builds across AI, product, ops, and infrastructure
Also shipped Nod, One, and Sidewalk, and led platform PM across video recommendation, people discovery, messaging, IoT irrigation, and blockchain analytics.