Senior QA Engineering / AI-assisted quality workflows

MateuszKoczorowski

Senior QA Engineer

Senior quality engineering for teams that need maintainable automation, clear release risk, and human-reviewed AI-assisted QA workflows.

Let's work together Selected experience

Quality ownership

Quality engineering that helps teams understand risk before it becomes release pressure.

I treat QA as technical ownership: understanding architecture, finding risk early, and turning testing into useful engineering feedback.

My work connects automation architecture, exploratory testing, API and integration validation, and release confidence. I use AI-assisted workflows for test design, debugging hypotheses, documentation structure, and review support, with human judgment kept at the center.

Frame quality through product risk, architecture, and release decisions.
Build automation that is maintainable, observable, and worth trusting.
Use AI to accelerate thinking while preserving review discipline and evidence.

Expertise

A focused quality practice across automation, architecture, delivery feedback, and risk.

The homepage keeps the signal high: the areas below are the core patterns I bring into product teams.

Automation

Test Automation

Designing maintainable automated checks across web, API, integration, and end-to-end layers so feedback is fast, readable, and trusted.

Quality

Quality Engineering

Building quality practices around risk, product behavior, exploratory testing, release confidence, and continuous improvement.

API testing

API & Integration Testing

Validating service contracts, data flows, integrations, and edge cases with API-focused checks and technical investigation.

CI/CD

CI/CD & Delivery Quality

Integrating automated checks into delivery pipelines and shaping quality gates that support decisions without blocking teams blindly.

AI QA

AI-assisted QA Workflows

Using AI as reviewed engineering support for test design, debugging, exploratory analysis, documentation, and repeatable QA workflows.

AI in QA

AI-assisted QA workflows built around orchestration, not replacement.

My AI work is organized into two deliberate QA engineering workflows: investigation and testing on one side, automation creation and review on the other.

The goal is not to hand testing over to a model. The value is orchestration: using AI to prepare structure, surface blind spots, and accelerate routine analysis while a human engineer owns risk, architecture, and release confidence.

I use OpenAI Codex, Claude Code, reusable review skills, and MCP integrations for Jira-driven analysis, application exploration, automation drafting, and documentation tied to delivery context.

Human-in-the-loop by design.

Human review stays present at every quality decision.
Workflow boundaries separate QA investigation from automation engineering.
MCP integrations connect tickets, code, browsers, and design context.

Workflow signal

OpenAI Codex

Used as part of a reviewed QA process, not as an autonomous replacement for engineering judgment.

Workflow signal

Claude Code

Used as part of a reviewed QA process, not as an autonomous replacement for engineering judgment.

Workflow signal

review skills

Used as part of a reviewed QA process, not as an autonomous replacement for engineering judgment.

Workflow signal

MCP

Used as part of a reviewed QA process, not as an autonomous replacement for engineering judgment.

Workflow signal

workflow boundaries

Used as part of a reviewed QA process, not as an autonomous replacement for engineering judgment.

Operating model

Two AI-assisted workflows, separated by engineering intent.

AI is used as structured support for analysis, exploration, planning, implementation, and review. The workflow boundary matters: investigation work should not be confused with automation creation.

AI-assisted Test Automation

Automation starts from product context: tickets, existing framework conventions, observed behavior, and reviewed Playwright or Cypress implementation plans.

AI-assisted Test Planning

Planning becomes stronger when AI structures tickets, acceptance criteria, risk areas, regression history, and unclear assumptions before the engineer decides priority and scope.

AI-assisted Review Workflows

Review workflows use AI as structured second-pass support for test intent, assertion quality, maintainability, negative paths, and ticket alignment.

Workflow 01

AI-assisted QA investigation workflow

Real QA execution work: ticket analysis, risk planning, MCP-assisted application exploration, validation, and structured defect reporting.

This workflow does not generate automation. It supports investigation, planning, testing, validation, and reporting.

AI helps structure context and reduce preparation time, but the QA engineer owns risk interpretation.

MCP tooling is used to observe the real product before finalizing conclusions.

The output is better investigation quality, clearer validation scope, and more actionable defect reporting.

Step 01

Jira ticket analysis

Analyze tickets, acceptance criteria, linked discussions, merge requests, code changes, and implementation context.

ENGINEERING WORKFLOWHUMAN REVIEW REQUIRED

Identify quality risks
Map impacted areas
Surface open questions

Step 02

Risk and test planning

Define regression scope, exploratory areas, manual validation paths, automation gaps, and testing priority.

QA EXECUTIONREVIEW DISCIPLINE

Prioritize risk
Separate manual and automated coverage
Clarify validation depth

Step 03

MCP-assisted application exploration

Use Playwright MCP to inspect real application behavior, UI states, browser behavior, flows, and edge cases.

MCP-ASSISTED

Validate assumptions
Compare ticket intent with implementation
Observe real product behavior

Step 04

QA execution

Perform exploratory testing, regression checks, existing automation validation, manual scenario verification, API validation, and cross-flow validation.

QA EXECUTIONHUMAN REVIEW REQUIRED

Execute scenarios
Validate integrations
Check regression-sensitive paths

Step 05

Defect reporting

Create structured Jira-ready defect reports with evidence, reproduction steps, implementation observations, and risk context.

REVIEW DISCIPLINE

Attach evidence
Include reproduction path
Explain engineering impact

Workflow 02

AI-assisted automation engineering workflow

Automation creation and review work: test analysis, application exploration, framework-aware draft generation, dedicated review, and final pull-request preparation.

This workflow is specifically for automation engineering. It starts from reviewed test scope and ends with implementation prepared for normal engineering review.

AI accelerates scaffolding, but architecture, test intent, and review quality remain engineering responsibilities.

Application exploration happens before code generation so automation reflects observed product behavior.

The output is maintainable automation prepared for normal pull request review, not one-click testing.

Step 01

Xray / Jira test analysis

Analyze existing test cases, acceptance criteria, reusable patterns, automation scope, and framework conventions.

ENGINEERING WORKFLOWHUMAN REVIEW REQUIRED

Review existing coverage
Identify reusable patterns
Confirm automation value

Step 02

Application exploration with Playwright MCP

Inspect real application behavior, selectors, flows, UI states, and edge cases before implementation.

MCP-ASSISTED

Validate selectors
Check UI state transitions
Observe edge paths

Step 03

Automation draft generation

Generate Playwright or Cypress test structure using existing framework architecture, fixtures, selectors, utilities, and patterns.

IMPLEMENTATION SUPPORT

Reuse fixtures
Follow local architecture
Avoid one-off test structure

Step 04

Automation review workflow

Review generated code with dedicated review skills and Codex reviewer validation for maintainability, assertions, negative paths, and framework consistency.

REVIEW DISCIPLINEHUMAN REVIEW REQUIRED

Validate assertions
Check maintainability
Review negative paths

Step 05

Final automation preparation

Refine implementation, improve readability, align with project conventions, and prepare for pull request review.

ENGINEERING WORKFLOW

Polish readability
Align naming
Prepare PR-ready code

Tooling context

MCP integrations support specific workflow moments.

Tooling is presented as connected engineering context, not as autonomous QA.

Atlassian MCP

Jira context can drive analysis, planning, automation scope, and defect reporting without constant manual copying.

Playwright MCP

Live application behavior can be inspected during planning, scenario validation, and automation design.

Chrome DevTools MCP

Browser inspection becomes part of QA investigation, from console and network evidence to clearer developer notes.

Figma MCP

Design context supports testing when it helps compare intended states, layouts, and interaction paths with implementation.

Experience timeline

A technical growth story across industrial systems, healthcare platforms, automation architecture, and release quality.

Company-grouped project history with expandable engineering detail: what needed validation, how quality systems were shaped, and where ownership improved release confidence.

Chapter 01

07.2024 - Present

STX Next

Senior QA EngineerPresent

Chapter 01

07.2024 - Present

STX Next

Senior QA EngineerPresent

AI, Data, and Cloud engineering consultancy with strong Python engineering roots, delivering scalable enterprise systems, digital platforms, and modern software solutions for international clients.

Role evolution

QA foundations into senior automation and quality ownership

07.2024 - 07.2025

Regular+ QA Engineer

Built trust through automation ownership, QA strategy work, and delivery impact across production systems.

07.2025 - Present

Senior QA Engineer

PromotedCurrent role

Promoted to Senior QA Engineer with broader ownership across automation architecture, mentoring, AI-assisted workflows, and quality strategy.

Process, mentoring, and impact

Promoted to Senior QA Engineer within the first year based on technical contribution, organizational impact, and strong performance.
Contributed as a Technical Recruiter, supporting hiring for QA engineering roles.
Contributed to AI-focused QA initiatives and internal quality engineering knowledge sharing.
Presented practical AI usage in QA during PAIDA Meetup as part of STX Next QA community activity.
Provided consultations for an internal AI bootcamp focused on practical software quality workflows.
Participated in Talent Navigator, a high-potential development program focused on leadership, growth, collaboration, and broader company impact.

Mentoring and community

Mentoring in automation testing and quality engineering practices.
Technical recruitment support for QA engineering roles.
Internal knowledge sharing around automation and AI-assisted QA workflows.

Project case studies

2 cases

02.2025 - Present

Present

Senior QA Engineer / automation coverage and migration support

Healthcare / Biotech / AI-driven Clinical Research

Active case study

AI-driven Clinical Trials Platform

AI-driven clinical trials platform operating under quality expectations typical for pharmaceutical and biotech environments.

Context

The platform needed scalable end-to-end regression coverage, maintainable automation, and careful release validation in a high-quality clinical research context.

Impact

Significantly increased automation coverage, improved maintainability of the automation architecture, and supported a scalable future testing strategy.

Technical ownership

Automation Architecture

Developed and maintained end-to-end coverage while supporting the migration path from Cypress toward a more scalable Playwright-based strategy.

CI/CD & Release Validation

Kept automated regression and manual release checks aligned with high-quality clinical research expectations and recurring delivery needs.

API & Integration Testing

Validated cross-system behavior around important product workflows, using integration-focused checks to support confidence beyond the UI layer.

Exploratory Testing

Used exploratory sessions to investigate product behavior around important workflows, complementing automation with human judgment.

Risk-based Testing

Prioritized validation around workflow risk, regression sensitivity, and areas where product changes needed careful release review.

AI-assisted Workflows

Worked in an AI-driven product context with attention to practical validation, review discipline, and maintainable human-in-the-loop QA workflows.

Responsibilities and QA strategy

Developed and maintained end-to-end automation tests.
Increased automated regression coverage across important user workflows.
Supported migration from Cypress to Playwright.
Performed manual and exploratory testing for release validation.
Worked in Scrum collaboration with product and engineering teams.

Automation, tooling, and stack

CypressPlaywrightTypeScriptEnd-to-End Testing

Engineering themes

#healthcare#biotech#AI platform#Playwright migration

Project name and client-identifying details are anonymized for public use.

07.2024 - 01.2025

Regular+ QA Engineer / automation and quality strategy ownership

Industrial Systems / Petrochemical Analytics

Industrial Monitoring & Risk Analysis Platform

Large-scale monitoring platform visualizing operational and sensor-driven industrial data, anomaly detection signals, and analytical workflows.

Context

The product required reliable validation of complex visualization, operational data, and analytical flows while the QA process needed stronger structure, ownership, and release confidence.

Impact

Improved QA process maturity, defect detection workflows, release confidence, and product stability without publishing unsupported numerical claims.

Technical ownership

Automation Architecture

Built the Cypress and TypeScript foundation, shaped test structure, and kept the suite oriented around maintainable coverage of product-critical workflows.

QA Strategy

Introduced clearer planning, test management, and quality process practices so the team could reason about risk before release pressure accumulated.

Data & Visualization Validation

Focused validation on analytical flows, operational data behavior, and visualization states where defects could distort product interpretation.

Risk-based Testing

Prioritized coverage around high-risk analytical and operational flows, balancing automation with manual investigation where system behavior needed context.

Exploratory Testing

Used exploratory testing to probe edge cases in visualization behavior, data interpretation, and product states that were difficult to express as stable automated checks.

Collaboration & Process Improvement

Worked with engineering and delivery stakeholders to make quality feedback more structured, actionable, and visible across the team.

Responsibilities and QA strategy

Designed and implemented a Cypress and TypeScript automation framework from scratch.
Owned major parts of the automation architecture and test structure.
Introduced QA strategy, testing metrics, and quality process improvements.
Organized test planning, test management workflows, and clearer test case structure.
Performed manual and exploratory testing across high-risk product areas.
Collaborated with engineering and delivery stakeholders on quality improvements.

Automation, tooling, and stack

CypressTypeScriptREST APIsData Visualization

Engineering themes

#automation architecture#industrial systems#risk analysis#test metrics

Project name and client-identifying details are anonymized for public use.

Chapter 02

03.2023 - 06.2024

Merixstudio

Regular QA Specialist

Chapter 02

03.2023 - 06.2024

Merixstudio

Regular QA Specialist

Technology consultancy combining product strategy, design, and software engineering to deliver scalable digital platforms and modern web applications for international clients.

Project case studies

3 cases

03.2023 - 04.2024

Regular QA Specialist / hardware-connected web platform validation

Industrial Systems / Hardware & Software Integration

Industrial Emergency Lighting Control Platform

Web-based platform integrated with dedicated physical emergency lighting hardware devices and embedded touch interfaces.

Context

Testing required validation across web application behavior, backend interactions, physical devices, embedded interfaces, and emergency or failover states.

Impact

Improved reliability validation, increased confidence in hardware/software integration quality, and expanded QA coverage for industrial workflows.

Technical ownership

Hardware & Software Validation

Validated web behavior together with physical device interfaces, embedded touch flows, and scenarios where hardware state changed product risk.

CI/CD & Environment Strategy

Supported GitLab CI execution against controlled infrastructure connected through VPN and VLAN access to physical devices.

Automation Ownership

Added Cypress coverage where web workflows could be automated while keeping manual validation for device-dependent scenarios.

Technical Challenges

Balanced application testing, backend interactions, and physical-device constraints in an environment where quality depended on the full system.

Risk-based Testing

Prioritized emergency, failover, integration, and device-state scenarios where defects would carry higher operational consequence.

Responsibilities and QA strategy

Performed manual testing of the web application and physical device interfaces.
Validated emergency, failover, and hardware/software integration scenarios.
Tested embedded touch interfaces and device-connected workflows.
Supported QA across frontend, backend, and hardware-connected systems.
Implemented Cypress automation for the web platform.
Supported automation execution through GitLab CI workers connected by VPN to dedicated VLAN infrastructure hosting physical devices.

Automation, tooling, and stack

Vue.jsTypeScriptPythonFastAPIC++LinuxCypress

Engineering themes

#hardware integration#industrial QA#Cypress#GitLab CI#VPN/VLAN

Project name, client, and device details are anonymized for public use.

06.2023 - 06.2024

Regular QA Specialist / automation framework owner

Sports Analytics / Data Visualization

Sports Field Analytics Platform

Web platform visualizing and analyzing measurement data for American football fields using external API data.

Context

The product needed reliable validation of external API data, visualization behavior, measurement accuracy, and regression-prone user workflows.

Impact

Improved regression reliability, increased automation coverage, and strengthened QA maturity and release confidence.

Technical ownership

Automation Architecture

Created a Cypress and JavaScript framework from scratch to cover regression-prone workflows and support repeatable delivery checks.

API & Integration Testing

Used Postman and integration-level investigation to validate external API behavior feeding the product's analytical experience.

CI/CD & Release Validation

Integrated automated checks into GitLab CI/CD so regression feedback became part of the delivery workflow.

Risk-based Testing

Focused test design on data flows, measurement interpretation, and regression-prone user journeys where external API behavior affected product trust.

Collaboration & Mentoring

Partnered with developers on QA improvements and supported junior QA engineers in practical automation and validation work.

Responsibilities and QA strategy

Performed manual testing across data visualization and user workflows.
Validated APIs using Postman and supported integration-level investigation.
Developed a Cypress and JavaScript automation framework from scratch.
Integrated automated checks into GitLab CI/CD pipelines.
Validated visualization behavior and measurement accuracy.
Collaborated with developers on QA improvements and mentored junior QA engineers.

Automation, tooling, and stack

ReactTypeScriptCypressPostmanREST APIsGitLab CI/CD

Engineering themes

#sports analytics#data visualization#API testing#automation framework

Project name and client-identifying details are anonymized for public use.

02.2024 - 03.2024

Regular QA Specialist / exploratory quality support

AI Analytics / Threat Analysis

AI-supported Threat Analysis Platform

AI-related analytics platform where QA work focused on exploratory validation, product behavior review, and practical investigation.

Context

The platform required careful exploratory QA around AI-supported analysis flows while keeping public details lightweight.

Impact

Supported quality discovery and feedback in an AI-related analytics product without exposing detailed client or platform information.

Technical ownership

Exploratory Testing

Investigated AI-related analytics behavior through structured exploratory work where detailed public examples need to remain limited.

Defect Investigation

Reviewed product behavior, captured findings clearly, and supported engineering follow-up without exposing client-specific details.

AI-assisted Quality Context

Focused on practical quality review around AI-supported workflows, keeping the public case study intentionally lightweight.

Risk-based Testing

Kept validation centered on analytical workflow risk, unclear behavior, and findings that needed structured engineering follow-up.

Responsibilities and QA strategy

Performed exploratory QA activities around AI-related analytics workflows.
Reviewed product behavior and supported defect investigation.
Kept findings structured for engineering follow-up.

Automation, tooling, and stack

AI AnalyticsExploratory TestingWeb Application Testing

Engineering themes

#AI analytics#exploratory testing#lightweight case study

This project is intentionally kept lightweight until additional public detail is approved.

Chapter 03

06.2021 - 02.2023

Colours Factory

System Support Specialist & Software Tester

Chapter 03

06.2021 - 02.2023

Colours Factory

System Support Specialist & Software Tester

Large-scale online printing and production ecosystem integrating e-commerce platforms, CMS systems, production workflows, print infrastructure, and partner operations.

Project case studies

1 case

System Support Specialist & Software Tester

E-commerce / Printing / Production Workflows

Online Printing Platform Quality & Operations

Large online printing platform spanning customer-facing e-commerce systems, CMS and backoffice workflows, preflighting, print validation, and partner migration.

Context

The ecosystem combined customer-facing behavior, production validation, partner workflows, database operations, and migration risk in a business-critical environment.

Impact

Built strong operational ownership, systems understanding, and early QA engineering foundations across a complex e-commerce and production environment.

Technical ownership

Operational Quality Ownership

Worked across customer-facing, backoffice, CMS, and production workflows where testing had to account for business operations as well as software behavior.

Migration & Release Risk

Owned technical parts of partner migration work, helping make repeatable processes clearer through documentation and system knowledge.

Database & Integration Support

Performed advanced PostgreSQL operations and supported validation around partner integrations and production-adjacent workflows.

QA Foundations

Built the foundation for later automation ownership by combining manual testing, support responsibility, documentation, and early Cypress practice.

Responsibilities and QA strategy

Tested customer-facing e-commerce systems and backoffice workflows.
Worked with CMS systems, preflighting flows, and print validation processes.
Supported partner migration from a legacy platform to a new system.
Owned technical aspects of the partner migration process.
Performed advanced PostgreSQL database operations.
Created technical documentation for repeatable operational work.
Performed manual testing and developed automation testing skills in Cypress.

Automation, tooling, and stack

Vue.jsTypeScriptPythonDjangoPostgreSQLCypress

Engineering themes

#e-commerce#printing workflows#database operations#partner migration

Operational details are summarized at a platform level and avoid partner-specific information.

Let's work together

Let's build reliable software with clearer risk, better automation, and calmer releases.

Open to selected QA leadership, automation architecture, AI-assisted QA workflow, mentoring, and speaking conversations.

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