MarcinM MarcinMikula

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

I test enterprise systems professionally and build open-source AI-assisted QA tools to explore practical applications of LLMs in software testing. My current work focuses on self-healing automation, defect analysis, and reliable evaluation of AI systems.

13+ yrs QA → Python / Playwright / pytest → AI-assisted automation → ???

🔭 What I'm building

Project	What it does
🔥 PhoenixQA	AI-assisted self-healing test automation framework with Safe/Autonomous execution, failure classification, and measurable healing benchmarks.
🐛 defect-pilot	Privacy-first Jira defect enrichment assistant that checks ticket completeness, identifies critical gaps, safely enriches available context, and routes actionable defects using explicit project rules.
🧪 llm-qa-toolkit	Configurable LLM evaluation framework for regulated domains — connects systems under evaluation and external evaluators through a validation engine that controls evidence, rules, assessment scope, and verdict boundaries.
🏗️ qa-automation-framework	The skeleton underneath it all — Playwright + pytest, POM/SOM, SQLAlchemy, enterprise telco/CRM/billing context

These aren't isolated demos — they're one ecosystem. qa-automation-framework is the chassis, PhoenixQA keeps its selectors alive, defect-pilot closes bugs against it, llm-qa-toolkit applies the same AI-QA thinking to a different problem: testing LLMs themselves.

🧠 How I work

I only put a technology on my CV once I've actually built something with it — no checkbox-collecting
My projects are heavily influenced by years spent testing billing, CRM and risk-management systems in large enterprise environments. Domain knowledge is one of my key strengths, and I bring that business context into every automation solution I build.
AI is a tool I use daily (Claude, Cursor) rather than a buzzword on a slide. Most projects above include an AI-assisted component that can be inspected directly in the code.
Pragmatic over theoretical: I'd rather ship a working POC with a LEARNINGS.md than a perfect architecture diagram that never runs

🌱 Currently exploring

Self-healing automation strategies — the core problem PhoenixQA is solving
LLM evaluation and benchmarking — Safe vs Autonomous healing accuracy
Agentic QA workflows — agents that read a bug ticket and act on it (defect-pilot)
Local vs cloud LLM trade-offs for regulated/enterprise test data

🛠️ Stack

PythonPlaywrightpytestSQLAlchemyJira APIOllamaClaude / Anthropic APIGitHub ActionsAllure

📍 Based in Warsaw — open to remote/hybrid roles

nofluffjobs profile · ⚽ Fan of AI, Python, and Spanish football

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

I test enterprise systems professionally and build open-source AI-assisted QA tools to explore practical applications of LLMs in software testing. My current work focuses on self-healing automation, defect analysis, and reliable evaluation of AI systems.

13+ yrs QA → Python / Playwright / pytest → AI-assisted automation → ???

🔭 What I'm building

Project	What it does
🔥 PhoenixQA	AI-assisted self-healing test automation framework with Safe/Autonomous execution, failure classification, and measurable healing benchmarks.
🐛 defect-pilot	Privacy-first Jira defect enrichment assistant that checks ticket completeness, identifies critical gaps, safely enriches available context, and routes actionable defects using explicit project rules.
🧪 llm-qa-toolkit	Configurable LLM evaluation framework for regulated domains — connects systems under evaluation and external evaluators through a validation engine that controls evidence, rules, assessment scope, and verdict boundaries.
🏗️ qa-automation-framework	The skeleton underneath it all — Playwright + pytest, POM/SOM, SQLAlchemy, enterprise telco/CRM/billing context

These aren't isolated demos — they're one ecosystem. qa-automation-framework is the chassis, PhoenixQA keeps its selectors alive, defect-pilot closes bugs against it, llm-qa-toolkit applies the same AI-QA thinking to a different problem: testing LLMs themselves.

🧠 How I work

I only put a technology on my CV once I've actually built something with it — no checkbox-collecting
My projects are heavily influenced by years spent testing billing, CRM and risk-management systems in large enterprise environments. Domain knowledge is one of my key strengths, and I bring that business context into every automation solution I build.
AI is a tool I use daily (Claude, Cursor) rather than a buzzword on a slide. Most projects above include an AI-assisted component that can be inspected directly in the code.
Pragmatic over theoretical: I'd rather ship a working POC with a LEARNINGS.md than a perfect architecture diagram that never runs

🌱 Currently exploring

Self-healing automation strategies — the core problem PhoenixQA is solving
LLM evaluation and benchmarking — Safe vs Autonomous healing accuracy
Agentic QA workflows — agents that read a bug ticket and act on it (defect-pilot)
Local vs cloud LLM trade-offs for regulated/enterprise test data

🛠️ Stack

PythonPlaywrightpytestSQLAlchemyJira APIOllamaClaude / Anthropic APIGitHub ActionsAllure

📍 Based in Warsaw — open to remote/hybrid roles

nofluffjobs profile · ⚽ Fan of AI, Python, and Spanish football

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

I test enterprise systems professionally and build open-source AI-assisted QA tools to explore practical applications of LLMs in software testing. My current work focuses on self-healing automation, defect analysis, and reliable evaluation of AI systems.

13+ yrs QA → Python / Playwright / pytest → AI-assisted automation → ???

🔭 What I'm building

Project	What it does
🔥 PhoenixQA	AI-assisted self-healing test automation framework with Safe/Autonomous execution, failure classification, and measurable healing benchmarks.
🐛 defect-pilot	Privacy-first Jira defect enrichment assistant that checks ticket completeness, identifies critical gaps, safely enriches available context, and routes actionable defects using explicit project rules.
🧪 llm-qa-toolkit	Configurable LLM evaluation framework for regulated domains — connects systems under evaluation and external evaluators through a validation engine that controls evidence, rules, assessment scope, and verdict boundaries.
🏗️ qa-automation-framework	The skeleton underneath it all — Playwright + pytest, POM/SOM, SQLAlchemy, enterprise telco/CRM/billing context

These aren't isolated demos — they're one ecosystem. qa-automation-framework is the chassis, PhoenixQA keeps its selectors alive, defect-pilot closes bugs against it, llm-qa-toolkit applies the same AI-QA thinking to a different problem: testing LLMs themselves.

🧠 How I work

I only put a technology on my CV once I've actually built something with it — no checkbox-collecting
My projects are heavily influenced by years spent testing billing, CRM and risk-management systems in large enterprise environments. Domain knowledge is one of my key strengths, and I bring that business context into every automation solution I build.
AI is a tool I use daily (Claude, Cursor) rather than a buzzword on a slide. Most projects above include an AI-assisted component that can be inspected directly in the code.
Pragmatic over theoretical: I'd rather ship a working POC with a LEARNINGS.md than a perfect architecture diagram that never runs

🌱 Currently exploring

Self-healing automation strategies — the core problem PhoenixQA is solving
LLM evaluation and benchmarking — Safe vs Autonomous healing accuracy
Agentic QA workflows — agents that read a bug ticket and act on it (defect-pilot)
Local vs cloud LLM trade-offs for regulated/enterprise test data

🛠️ Stack

PythonPlaywrightpytestSQLAlchemyJira APIOllamaClaude / Anthropic APIGitHub ActionsAllure

📍 Based in Warsaw — open to remote/hybrid roles

nofluffjobs profile · ⚽ Fan of AI, Python, and Spanish football

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

I test enterprise systems professionally and build open-source AI-assisted QA tools to explore practical applications of LLMs in software testing. My current work focuses on self-healing automation, defect analysis, and reliable evaluation of AI systems.

13+ yrs QA → Python / Playwright / pytest → AI-assisted automation → ???

🔭 What I'm building

Project	What it does
🔥 PhoenixQA	AI-assisted self-healing test automation framework with Safe/Autonomous execution, failure classification, and measurable healing benchmarks.
🐛 defect-pilot	Privacy-first Jira defect enrichment assistant that checks ticket completeness, identifies critical gaps, safely enriches available context, and routes actionable defects using explicit project rules.
🧪 llm-qa-toolkit	Configurable LLM evaluation framework for regulated domains — connects systems under evaluation and external evaluators through a validation engine that controls evidence, rules, assessment scope, and verdict boundaries.
🏗️ qa-automation-framework	The skeleton underneath it all — Playwright + pytest, POM/SOM, SQLAlchemy, enterprise telco/CRM/billing context

These aren't isolated demos — they're one ecosystem. qa-automation-framework is the chassis, PhoenixQA keeps its selectors alive, defect-pilot closes bugs against it, llm-qa-toolkit applies the same AI-QA thinking to a different problem: testing LLMs themselves.

🧠 How I work

I only put a technology on my CV once I've actually built something with it — no checkbox-collecting
My projects are heavily influenced by years spent testing billing, CRM and risk-management systems in large enterprise environments. Domain knowledge is one of my key strengths, and I bring that business context into every automation solution I build.
AI is a tool I use daily (Claude, Cursor) rather than a buzzword on a slide. Most projects above include an AI-assisted component that can be inspected directly in the code.
Pragmatic over theoretical: I'd rather ship a working POC with a LEARNINGS.md than a perfect architecture diagram that never runs

🌱 Currently exploring

Self-healing automation strategies — the core problem PhoenixQA is solving
LLM evaluation and benchmarking — Safe vs Autonomous healing accuracy
Agentic QA workflows — agents that read a bug ticket and act on it (defect-pilot)
Local vs cloud LLM trade-offs for regulated/enterprise test data

🛠️ Stack

PythonPlaywrightpytestSQLAlchemyJira APIOllamaClaude / Anthropic APIGitHub ActionsAllure

📍 Based in Warsaw — open to remote/hybrid roles

nofluffjobs profile · ⚽ Fan of AI, Python, and Spanish football

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

I test enterprise systems professionally and build open-source AI-assisted QA tools to explore practical applications of LLMs in software testing. My current work focuses on self-healing automation, defect analysis, and reliable evaluation of AI systems.

13+ yrs QA → Python / Playwright / pytest → AI-assisted automation → ???

🔭 What I'm building

Project	What it does
🔥 PhoenixQA	AI-assisted self-healing test automation framework with Safe/Autonomous execution, failure classification, and measurable healing benchmarks.
🐛 defect-pilot	Privacy-first Jira defect enrichment assistant that checks ticket completeness, identifies critical gaps, safely enriches available context, and routes actionable defects using explicit project rules.
🧪 llm-qa-toolkit	Configurable LLM evaluation framework for regulated domains — connects systems under evaluation and external evaluators through a validation engine that controls evidence, rules, assessment scope, and verdict boundaries.
🏗️ qa-automation-framework	The skeleton underneath it all — Playwright + pytest, POM/SOM, SQLAlchemy, enterprise telco/CRM/billing context

These aren't isolated demos — they're one ecosystem. qa-automation-framework is the chassis, PhoenixQA keeps its selectors alive, defect-pilot closes bugs against it, llm-qa-toolkit applies the same AI-QA thinking to a different problem: testing LLMs themselves.

🧠 How I work

I only put a technology on my CV once I've actually built something with it — no checkbox-collecting
My projects are heavily influenced by years spent testing billing, CRM and risk-management systems in large enterprise environments. Domain knowledge is one of my key strengths, and I bring that business context into every automation solution I build.
AI is a tool I use daily (Claude, Cursor) rather than a buzzword on a slide. Most projects above include an AI-assisted component that can be inspected directly in the code.
Pragmatic over theoretical: I'd rather ship a working POC with a LEARNINGS.md than a perfect architecture diagram that never runs

🌱 Currently exploring

Self-healing automation strategies — the core problem PhoenixQA is solving
LLM evaluation and benchmarking — Safe vs Autonomous healing accuracy
Agentic QA workflows — agents that read a bug ticket and act on it (defect-pilot)
Local vs cloud LLM trade-offs for regulated/enterprise test data

🛠️ Stack

PythonPlaywrightpytestSQLAlchemyJira APIOllamaClaude / Anthropic APIGitHub ActionsAllure

📍 Based in Warsaw — open to remote/hybrid roles

nofluffjobs profile · ⚽ Fan of AI, Python, and Spanish football

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

I test enterprise systems professionally and build open-source AI-assisted QA tools to explore practical applications of LLMs in software testing. My current work focuses on self-healing automation, defect analysis, and reliable evaluation of AI systems.

13+ yrs QA → Python / Playwright / pytest → AI-assisted automation → ???

🔭 What I'm building

Project	What it does
🔥 PhoenixQA	AI-assisted self-healing test automation framework with Safe/Autonomous execution, failure classification, and measurable healing benchmarks.
🐛 defect-pilot	Privacy-first Jira defect enrichment assistant that checks ticket completeness, identifies critical gaps, safely enriches available context, and routes actionable defects using explicit project rules.
🧪 llm-qa-toolkit	Configurable LLM evaluation framework for regulated domains — connects systems under evaluation and external evaluators through a validation engine that controls evidence, rules, assessment scope, and verdict boundaries.
🏗️ qa-automation-framework	The skeleton underneath it all — Playwright + pytest, POM/SOM, SQLAlchemy, enterprise telco/CRM/billing context

These aren't isolated demos — they're one ecosystem. qa-automation-framework is the chassis, PhoenixQA keeps its selectors alive, defect-pilot closes bugs against it, llm-qa-toolkit applies the same AI-QA thinking to a different problem: testing LLMs themselves.

🧠 How I work

I only put a technology on my CV once I've actually built something with it — no checkbox-collecting
My projects are heavily influenced by years spent testing billing, CRM and risk-management systems in large enterprise environments. Domain knowledge is one of my key strengths, and I bring that business context into every automation solution I build.
AI is a tool I use daily (Claude, Cursor) rather than a buzzword on a slide. Most projects above include an AI-assisted component that can be inspected directly in the code.
Pragmatic over theoretical: I'd rather ship a working POC with a LEARNINGS.md than a perfect architecture diagram that never runs

🌱 Currently exploring

Self-healing automation strategies — the core problem PhoenixQA is solving
LLM evaluation and benchmarking — Safe vs Autonomous healing accuracy
Agentic QA workflows — agents that read a bug ticket and act on it (defect-pilot)
Local vs cloud LLM trade-offs for regulated/enterprise test data

🛠️ Stack

PythonPlaywrightpytestSQLAlchemyJira APIOllamaClaude / Anthropic APIGitHub ActionsAllure

📍 Based in Warsaw — open to remote/hybrid roles

nofluffjobs profile · ⚽ Fan of AI, Python, and Spanish football

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

I test enterprise systems professionally and build open-source AI-assisted QA tools to explore practical applications of LLMs in software testing. My current work focuses on self-healing automation, defect analysis, and reliable evaluation of AI systems.

13+ yrs QA → Python / Playwright / pytest → AI-assisted automation → ???

🔭 What I'm building

Project	What it does
🔥 PhoenixQA	AI-assisted self-healing test automation framework with Safe/Autonomous execution, failure classification, and measurable healing benchmarks.
🐛 defect-pilot	Privacy-first Jira defect enrichment assistant that checks ticket completeness, identifies critical gaps, safely enriches available context, and routes actionable defects using explicit project rules.
🧪 llm-qa-toolkit	Configurable LLM evaluation framework for regulated domains — connects systems under evaluation and external evaluators through a validation engine that controls evidence, rules, assessment scope, and verdict boundaries.
🏗️ qa-automation-framework	The skeleton underneath it all — Playwright + pytest, POM/SOM, SQLAlchemy, enterprise telco/CRM/billing context

These aren't isolated demos — they're one ecosystem. qa-automation-framework is the chassis, PhoenixQA keeps its selectors alive, defect-pilot closes bugs against it, llm-qa-toolkit applies the same AI-QA thinking to a different problem: testing LLMs themselves.

🧠 How I work

I only put a technology on my CV once I've actually built something with it — no checkbox-collecting
My projects are heavily influenced by years spent testing billing, CRM and risk-management systems in large enterprise environments. Domain knowledge is one of my key strengths, and I bring that business context into every automation solution I build.
AI is a tool I use daily (Claude, Cursor) rather than a buzzword on a slide. Most projects above include an AI-assisted component that can be inspected directly in the code.
Pragmatic over theoretical: I'd rather ship a working POC with a LEARNINGS.md than a perfect architecture diagram that never runs

🌱 Currently exploring

Self-healing automation strategies — the core problem PhoenixQA is solving
LLM evaluation and benchmarking — Safe vs Autonomous healing accuracy
Agentic QA workflows — agents that read a bug ticket and act on it (defect-pilot)
Local vs cloud LLM trade-offs for regulated/enterprise test data

🛠️ Stack

PythonPlaywrightpytestSQLAlchemyJira APIOllamaClaude / Anthropic APIGitHub ActionsAllure

📍 Based in Warsaw — open to remote/hybrid roles

nofluffjobs profile · ⚽ Fan of AI, Python, and Spanish football

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

I test enterprise systems professionally and build open-source AI-assisted QA tools to explore practical applications of LLMs in software testing. My current work focuses on self-healing automation, defect analysis, and reliable evaluation of AI systems.

13+ yrs QA → Python / Playwright / pytest → AI-assisted automation → ???

🔭 What I'm building

Project	What it does
🔥 PhoenixQA	AI-assisted self-healing test automation framework with Safe/Autonomous execution, failure classification, and measurable healing benchmarks.
🐛 defect-pilot	Privacy-first Jira defect enrichment assistant that checks ticket completeness, identifies critical gaps, safely enriches available context, and routes actionable defects using explicit project rules.
🧪 llm-qa-toolkit	Configurable LLM evaluation framework for regulated domains — connects systems under evaluation and external evaluators through a validation engine that controls evidence, rules, assessment scope, and verdict boundaries.
🏗️ qa-automation-framework	The skeleton underneath it all — Playwright + pytest, POM/SOM, SQLAlchemy, enterprise telco/CRM/billing context

These aren't isolated demos — they're one ecosystem. qa-automation-framework is the chassis, PhoenixQA keeps its selectors alive, defect-pilot closes bugs against it, llm-qa-toolkit applies the same AI-QA thinking to a different problem: testing LLMs themselves.

🧠 How I work

I only put a technology on my CV once I've actually built something with it — no checkbox-collecting
My projects are heavily influenced by years spent testing billing, CRM and risk-management systems in large enterprise environments. Domain knowledge is one of my key strengths, and I bring that business context into every automation solution I build.
AI is a tool I use daily (Claude, Cursor) rather than a buzzword on a slide. Most projects above include an AI-assisted component that can be inspected directly in the code.
Pragmatic over theoretical: I'd rather ship a working POC with a LEARNINGS.md than a perfect architecture diagram that never runs

🌱 Currently exploring

Self-healing automation strategies — the core problem PhoenixQA is solving
LLM evaluation and benchmarking — Safe vs Autonomous healing accuracy
Agentic QA workflows — agents that read a bug ticket and act on it (defect-pilot)
Local vs cloud LLM trade-offs for regulated/enterprise test data

🛠️ Stack

PythonPlaywrightpytestSQLAlchemyJira APIOllamaClaude / Anthropic APIGitHub ActionsAllure

📍 Based in Warsaw — open to remote/hybrid roles

nofluffjobs profile · ⚽ Fan of AI, Python, and Spanish football

MarcinM MarcinMikula

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

🔭 What I'm building

🧠 How I work

🌱 Currently exploring

🛠️ Stack

📍 Based in Warsaw — open to remote/hybrid roles

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MarcinM MarcinMikula

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

🔭 What I'm building

🧠 How I work

🌱 Currently exploring

🛠️ Stack

📍 Based in Warsaw — open to remote/hybrid roles

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MarcinM MarcinMikula

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

🔭 What I'm building

🧠 How I work

🌱 Currently exploring

🛠️ Stack

📍 Based in Warsaw — open to remote/hybrid roles

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MarcinM MarcinMikula

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

🔭 What I'm building

🧠 How I work

🌱 Currently exploring

🛠️ Stack

📍 Based in Warsaw — open to remote/hybrid roles

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MarcinM MarcinMikula

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

🔭 What I'm building

🧠 How I work

🌱 Currently exploring

🛠️ Stack

📍 Based in Warsaw — open to remote/hybrid roles

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MarcinM MarcinMikula

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

🔭 What I'm building

🧠 How I work

🌱 Currently exploring

🛠️ Stack

📍 Based in Warsaw — open to remote/hybrid roles

Pinned Loading

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MarcinM MarcinMikula

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

🔭 What I'm building

🧠 How I work

🌱 Currently exploring

🛠️ Stack

📍 Based in Warsaw — open to remote/hybrid roles

Pinned Loading

Uh oh!

MarcinM MarcinMikula

QA Engineer building AI-powered test automation — 13+ years across telco, banking & insurance, now turning that domain depth into autonomous testing tools.

🔭 What I'm building

🧠 How I work

🌱 Currently exploring

🛠️ Stack

📍 Based in Warsaw — open to remote/hybrid roles

Pinned Loading

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