30K+
community grown
Twitter/X audience built for a Web3 gaming product
AI Product Engineer
Building useful AIproducts frommessy signals.
I connect product judgment, growth experiments, data work, and technical fluency to shape useful AI-enabled products.
product judgmentgrowth evidencedata systemsAI prototype thinking
AI Product Lab
prototype signals in motion
Nuval
field ops
photo / voice / QRverified work
Hanio
hotel PMS
occupancy / room / seasonprice suggestion
Nuvida
student life
courses / exams / notesstudy plan
Product interface
AI workflow sketch
Prompt frame
Turn scattered user behavior into a testable AI product decision.
Inputmessy user signalcaptured
Reasoningproduct hypothesisframed
Prototypesmallest useful AI loopscoped
Metricadoption + claritytracked
Evaluation
Does it shorten a real workflow?
Can the user verify the output?
Is the success signal measurable?
20+
tables per week
legacy utility data moved through migration workflows
106
games submitted
remote university game jam enabled through systems and partners
3
active builds
Nuval, Hanio, and Nuvida framed as honest product lab work
About
Early-career, but already shaped by real systems.
I am a 25-year-old university student at Bogazici University, building toward AI Product Engineering from a practical base: growth work that taught me acquisition loops, data migration work that taught me systems thinking, and product-facing projects that taught me to care about what users actually do.
Builder profile
Product-minded, technical enough to prototype, honest enough to measure.
The point is not to look like a finished senior AI expert. The point is to show a credible direction: useful workflows, clear user problems, data-aware decisions, and enough technical fluency to move from idea to working shape.
Base
Bogazici University
Direction
AI Product Engineering
Style
Evidence-first
Positioning
AI Product Engineer
I am aiming at the space where AI capability, product taste, usable workflows, and measurable adoption meet.
Education
Bogazici University
Computer and Educational Technology background with practical work across web, growth, data, and communities.
Working style
Evidence before theater
I prefer small tests, clear metrics, user language, and honest case studies over inflated claims.
Current focus
AI product practice
My current focus is learning how AI products move from rough user problem to usable prototype, measured workflow, and clear product decision.
Work
Case studies without pretending they are AI trophies.
The evidence is real: growth loops, data systems, web operations, and leadership. The framing is where I am heading - AI products that are useful, measurable, and grounded in user behavior.
Read for
how I identify signals in messy environments
Look for
small systems that turn activity into feedback
Judge by
evidence, restraint, and product relevance
Growth SystemsProduct Thinking
Early Community Growth Loop
Gamcap Labs / Marketing Intern / Jan 2023 - Aug 2023
Helped take a crypto gaming community from zero to a visible launch audience through creator partnerships, community rituals, and performance tracking.
Signal
Cold launch, no trust
Action
Creator-led growth loop
Evidence
30K+ audience
Product value
Validated early demand
Signal behind the work
A new gaming product needed early attention and trust in a skeptical, crowded Web3 market with limited budget.
Action taken
Mapped where target users already spent time, tested creator partnerships, ran lightweight engagement loops, and used community response as a feedback signal.
Product relevance
Shows how I think about acquisition as a product system: promise, channel, feedback, trust, and measurable behavior.
Evidence
- 0 to 30K Twitter/X followers in 8 months
- 2K-member Discord community built from scratch
- 10+ creator partnerships coordinated
- roughly 1,000 qualified waitlist signups
Tools
Twitter/XDiscordGoogle AdsMeta AdsSpreadsheets
Data & AnalyticsGrowth Systems
Performance Marketing Diagnostics
Dugun.com / Marketing Intern / Oct 2023 - Nov 2023
Supported paid and organic performance work for a high-intent marketplace, turning campaign data into clearer reporting and optimization recommendations.
Signal
Acquisition efficiency
Action
Campaign diagnostics
Evidence
Weekly KPI reports
Product value
Clearer growth decisions
Technical FluencyProduct Thinking
Department Web and Content Operations
Bogazici University / Social Media & Website Manager / 4 months, part-time
Maintained a department's web and social presence during remote learning, combining content operations with hands-on legacy web maintenance.
Signal
Remote student friction
Action
Web + content ops
Evidence
10+ fixes shipped
Product value
More reliable updates
Data & AnalyticsTechnical Fluency
Legacy Data Migration Workflow
Creo Technologies / Data Migration Specialist / 3 months
Worked in a distributed data team moving high-volume electricity company records from legacy systems into a modernized workflow.
Signal
Messy operational data
Action
ETL handoff workflow
Evidence
20+ tables weekly
Product value
Cleaner data foundation
LeadershipProduct Thinking
Remote Game Jam System
Bogazici University Computer Club / Game Development Lead / Aug 2020 - Aug 2021
Led the systems and partnerships behind a remote-first student game development program during the pandemic.
Signal
Community lost venue
Action
Remote build system
Evidence
106 games shipped
Product value
Momentum under constraint
Product Lab
Active builds, framed honestly.
Nuval, Hanio, and Nuvida show where I am turning real workflow problems into AI-assisted product systems. They are not presented as traction stories; they are evidence of product direction, scope, and build taste.
Active build
Nuval
Tracks the work, not the phone. Makes verifiable production visible instead of treating presence as proof.
Status
Field ops prototype
Maturity
Build in progress
Claim
No traction claimed
Problem
Managers in construction, hotel room setup, and fit-out projects often learn too late who is actually progressing, which room is blocked, and whether reported hours are trustworthy.
Who it is for
Subcontractors and project teams working across many rooms or zones: owners, foremen, workers, and client-side project managers.
AI/product angle
Combines QR, geofence, room plans, checklists, photos, voice notes, and timesheets. AI turns field notes into tasks, matches evidence to room progress, flags unverifiable time, and surfaces the five problems that matter today.
Current state
Product logic is clear. Supabase schema, RLS/auth direction, QR-geofence timesheet foundation, and mobile role separation have started.
Next validation step
Center the product around daily work packages, photo/voice evidence, the AI field assistant, foreman approval queue, and unverifiable time review.
SupabaseRLS/AuthQRGeofenceMobile roles
Skills
A capability map for AI product work.
A clearer split between product judgment, AI workflow design, technical build fluency, data sense, and the modern tools I use to move faster.
AI systems
turn messy inputs into verified outputs
Product build
scope useful prototypes before big claims
Technical data
understand the stack and the signals
Core
Product Strategy
Choosing the right problem, user, promise, and success signal.
user problem framingMVP scopingpositioningworkflow mappingstakeholder clarity
Core
AI Workflow Design
Turning messy inputs into useful, verifiable AI-assisted outputs.
prompt iterationAI assistant flowshuman-in-the-loop reviewevaluation mindsetRAG planning
Support
Technical Build Stack
Enough implementation fluency to prototype and speak with engineers.
React / TypeScriptFlutterSupabase / FirebaseGemini APIHTML / CSS
Support
Data & Growth
Reading behavior, quality, and adoption signals before making claims.
SQL basicsETL workflowsfunnel thinkingcampaign diagnosticsdashboard thinking
Support
AI Build Tools
Using modern tools to move faster without pretending the tool is the product.
CursorClaude CodeChatGPTGeminiNotion
Contact
Useful AI products need clear problems and sharp teams.
I am open to junior AI product engineer, product, growth, and data-adjacent roles where I can learn fast and contribute with evidence.