~/meabed$whoamistartup CTO · AI software engineer

Startup CTO,
hands-on AI engineer.

Mo (Mohamed Meabed)·github.com/meabed·in/mmeabed·view resume
Startup CTOAI software engineerHands-on leadershipProductive teamsSystems thinkerEverything as code0-1 to scalePragmatic deliveryReliable software
Mohamed Meabed

I'm Mo, Mohamed Meabed. I work where product ambition meets systems reality: the first useful version, the platform that grows with the business, and the team that has to keep improving it after launch.

Right now that means production AI and the engineering around it: data quality, evals, generated contracts, integration tests, model workflows, cost curves, and the practical paths that make the product useful when the input is messy, the network is weak, and the team needs to keep moving.

I like clear boundaries, readable code, short feedback loops, and changes small enough to verify quickly. I stay close to the code because hands-on leadership is how the architecture, team rhythm, and product promise stay connected.

How I work
Field reality first
Start with where the product is used: weak networks, old devices, busy operators, messy data, and the trust people need to keep doing their work.
Fast feedback loops
Type checks, tests, logs, API calls, and dev environments should answer quickly enough for engineers to learn while the context is still fresh.
Reliability is design
Retries, idempotency, upserts, backpressure, CI/CD, and defensive programming are product work. They create confidence for teams that need to move quickly.
Everything as code
Repos, infra, local machines, release paths, and repeated setup should become reviewable, idempotent code once the team depends on them.
Small change surface
Keep a fast pace by shipping the smallest useful move, verifying it, and keeping the next decision easy to reason about.
Reality stays in the loop
Logs, traces, costs, support tickets, and user feedback keep architecture, leadership, and product choices honest.
Excellence is repeatable
Good engineering is not ceremony. It is a set of habits that make the next decision clearer, the next deploy calmer, and the next engineer more effective.
Currently
Building the platform at XpertSea, writing down the engineering decisions I keep seeing twice, and shipping small tools in the Lab →
Working range
Production AIPlatform architectureFeedback loopsReliabilityCost disciplineDeveloper toolingData qualityGenerated contractsEverything as codeIntegration testsTeam systemsSecurityDelivery processProduct engineeringOperating rhythm
Where I've built

Different markets, same pattern: ship useful product, keep the platform readable, and give the team enough clarity to move fast without guessing.

2022–now
CTO·XpertSea

Production AI and farm operations: improved reliability for low-connectivity field use, strengthened CI/CD and integration coverage, modernized model workflows, and made AI useful in daily field work.

2021–22
Sr Development Manager·Loblaw Digital

Retail-scale delivery: marketplace and Joe Fresh engineering, vendor workflows, incident learning, stakeholder clarity, and the operating habits that keep teams moving with shared context.

2019–21
CTO·Tradeling

B2B marketplace from first release to daily trade: team building, payment readiness, catalog/search/data workflows, and the operating discipline real businesses depend on.

2015–18
Co-founder & CTO·Tajawal

Travel platform and engineering organization: web/mobile, backoffice, supplier integrations, payments, data, DevOps, security, and a distributed team that needed a clear operating rhythm.

2013–15
Software Engineer·Westwing

E-commerce, customer operations, and warehouse workflows where correctness, inventory visibility, and support clarity mattered more than cleverness.

Lab
Small repos, notes, and experiments. Explore →
AI & LLMs
Repo rules, prompts, and agent workflows from real work. Open →