AI/Product Validation
Define evaluation criteria, test cases, edge cases, failure categories, launch-readiness checks, and human-review loops for AI and product features.
Berlin-based technical product operator
Technical Product & AI Solutions Operator
I help AI and technology teams turn ambiguous product, customer, and technical problems into clear workflows, validation systems, and execution-ready solutions.
Nearly a decade across QA automation, Python, APIs, IoT, CI/CD, and product-quality systems, now focused on AI Product, Solutions Engineering, Technical Product, and Product Operations roles.
AI Product ReadinessSolutions EngineeringLLM ValidationPython · APIs · IoTBerlin, Germany
Foundation
QA was the foundation, not the ceiling.
My QA automation background trained me to think in systems: how workflows break, where users struggle, how APIs fail, how edge cases appear, and how teams can build confidence before release. I now apply that foundation closer to product decisions, AI validation, customer workflows, and solution design.
What I can own
Work you can hand me next Monday
Practical ownership across LLM evaluation, product audits, technical solutioning, and release-ready documentation.
Customer & Workflow Discovery
Map user journeys, clarify requirements, identify friction points, and translate user needs into product and technical recommendations.
Technical Solutioning
Understand APIs, integrations, data flows, system behavior, and implementation risks so product and customer-facing teams can make better decisions.
Execution & Documentation
Turn messy discussions into clear notes, decisions, owners, risks, next steps, and execution plans.
My edge
I turn failure points into product decisions.
The differentiator is not QA as a title. It is the habit of finding failure points, clarifying requirements, designing checks, and building product confidence before users feel the risk.
Technical depth
I understand APIs, automation, data flows, release risk, and integration behavior.
Product judgment
I translate messy inputs into requirements, tradeoffs, and product-ready recommendations.
Customer/workflow understanding
I map user journeys, friction points, support risk, and adoption blockers.
Execution discipline
I make owners, decisions, launch criteria, risks, and next steps visible.
Where I create value
Product readiness, solution clarity, and follow-through
I make product, AI, and technical decisions easier to evaluate, explain, and execute.
Clarify Product Problems
I break unclear product or customer problems into users, requirements, constraints, risks, and launch decisions.
Validate AI & System Quality
I define how to check whether AI outputs, API behavior, and user flows are reliable enough for real users.
Translate Between Product and Engineering
I connect user needs, technical constraints, API behavior, and implementation risks in language different teams can act on.
Turn Insights Into Execution
I convert findings into priorities, owners, next steps, documentation, and release-ready follow-through.
Proof of work
Evidence, not just positioning
Examples across LLM evaluation, product workflow audit, API validation, release risk, and AI feature readiness.
A practical structure to evaluate whether AI outputs are accurate, consistent, useful, and ready for users.
LLM Evaluation Framework
- Problem
- AI features can look impressive in demos but fail in real usage when outputs become inconsistent, inaccurate, slow, or difficult to evaluate.
- Approach
- Defined evaluation dimensions such as accuracy, hallucination risk, consistency, latency, user-intent match, edge cases, scoring criteria, and human-review loops.
- Value
- Moves AI quality discussions from subjective opinions to measurable product-readiness decisions.
- Output
- Evaluation rubric, test-case structure, failure categories, launch-readiness checklist.
AI Product ManagerSolutions EngineerTechnical Product
A product audit converting a messy user journey into friction points, priorities, and roadmap recommendations.
Product Workflow Audit
- Problem
- A knowledge-saving product needed clearer onboarding, saving flows, dashboard structure, user motivation, and retention logic.
- Approach
- Audited onboarding, save flow, dashboard experience, user motivation, and retention loops; converted findings into prioritized product recommendations.
- Value
- Clarified what to improve first, where the user journey breaks, and how the product could increase activation and retention.
- Output
- User-flow audit, friction map, prioritized recommendations, roadmap inputs.
AI Product ManagerProduct OperationsTechnical Product
A reliability approach for complex systems where APIs, UI flows, backend services, and device data need to work together.
Technical Risk & Release Validation
- Problem
- IoT and cloud-connected systems often fail across integration points: device data, APIs, UI flows, backend services, and release environments.
- Approach
- Designed and executed validation strategies using Python, API testing, regression checks, integration testing, and cross-functional engineering feedback.
- Value
- Improved release confidence, earlier risk detection, and more reliable behavior across technical workflows.
- Output
- Regression test strategy, API validation checks, release-risk checklist, integration test coverage.
Solutions EngineerTechnical ProductProduct Quality
A decision structure for choosing whether to build an AI feature, automate a workflow, or improve the process first.
AI Feature Decision Memo
- Problem
- Teams often rush to add AI before understanding whether the user workflow, data quality, and business process are ready.
- Approach
- Created a decision structure comparing user pain, process maturity, risk, effort, data availability, reliability requirements, and measurable success criteria.
- Value
- Helps teams make clearer build-vs-automate-vs-wait decisions before committing engineering effort.
- Output
- Decision memo, readiness criteria, risk matrix, success metrics.
AI Product ManagerProduct OperationsTechnical Product
Selected artifacts
Reusable outputs for product, AI, and technical decisions
Artifacts I can create to help teams make better product, AI, and technical decisions.
LLM Evaluation Rubric
Scoring criteria for accuracy, hallucination risk, consistency, latency, user-intent match, and user usefulness.
Use when an AI feature needs a measurable quality bar.
Sample coming soonProduct Audit Memo
A structured memo turning user-flow observations into friction points, product opportunities, and prioritized recommendations.
Use when a product journey feels busy but the next improvement is unclear.
Sample coming soonAI Feature Readiness Checklist
A checklist to evaluate whether a workflow, dataset, user need, and reliability threshold are ready for an AI feature.
Use before committing engineering effort to an AI build.
Sample coming soonTechnical Risk Checklist
A release and solution-readiness checklist covering APIs, integrations, data flows, edge cases, and user-impact risks.
Use before pilots, handoffs, demos, or customer-facing launches.
Sample coming soonHow I work
From finding to launch criteria
A lightweight process for turning product signals, technical risks, and customer friction into usable outputs.
Make vague problems concrete
Start with the user, requirement, constraint, risk, and evidence needed.
Map the system before the solution
Map dependencies, data quality, API behavior, feedback loops, and failure modes.
Communicate clearly
Turn scattered discussion into decision notes, launch criteria, and execution plans.
Care about reliability
Ask whether the product will work for real users after the demo is over.
Skills & tools
The toolkit behind the work
Product & Strategy
Product discoveryUser journeysProblem framingPrioritizationDecision memosWorkflow analysisStakeholder alignmentLaunch criteria
AI & Validation
LLM evaluationPrompt testingHallucination checksTest datasetsReliability scoringAI output regressionHuman review loopsEdge cases
Technical Systems
PythonSeleniumPytestPandasRequestsREST APIsGraphQLMQTTCI/CDGitHubLinuxPostmanAWS SDKIoT systems
Execution & Operations
DocumentationOperating cadenceRelease validationCross-functional workAgile deliveryMentoringRisk analysisDecision tracking
Background
Background
I bring a decade-long foundation in QA automation, technical validation, and product-quality systems across engineering teams, with hands-on experience in Python, APIs, IoT, CI/CD, and cross-functional delivery. My next chapter is focused on AI Product, Solutions Engineering, Technical Product, and Product Operations roles where complex problems need both structure and momentum.
AI Product ManagerSolutions EngineerTechnical ProductProduct OperationsProduct QualityAI Validation