Amro Moustafa

about me über mich

ADAS validation and systems integration engineer.

4+ years in ADAS validation — HIL / SiL / MiL, ISO 26262 & SOTIF
At EDAG Group (embedded at CARIAD / VW Group), Berlin, since 2023
Builds automation-first CI/CD toolchains — triage time cut from 20 min to 3 min
Expert in Python, MATLAB/Simulink/Stateflow, CANalyzer/CANoe & DOORS
ISTQB® CTFL 4.0 certified; Prototype Driving Licence (Germany)
Arabic (native) · German C1+ · English C1+ — open to Berlin or KAEC roles

email

amro.mo.moustafa@gmail.com

phone telefon

+49 1590 1355023

location standort

Berlin, Germany

Years in
Automotive Safety

25%

Faster CI/CD
Test Cycles

Faster Defect
Triage (LLM+ML)

OEM Vehicle
Platforms

companies i have worked with

experience berufserfahrung

EDAG Group — embedded across Tier 1 OEM programmes CURRENT

Professional Software Engineer — ADAS Validation, Functional Safety & Systems Integration

05/2023 – Present

Berlin, Germany

End-to-end software integration ownership
served as technical anchor for ADAS validation programmes across Tier 1 OEM customers; responsible for the full pipeline from requirements decomposition through branch coordination, integration test execution, defect triage, and release evidence delivery; held direct accountability for integration quality at programme milestone reviews.
CI/CD pipeline design & quality gates
architected scalable GitLab CI / Docker pipelines (Python + Bash), enforcing smoke tests, regression coverage, and automated quality-gate criteria across multiple vehicle software platforms; 25 % reduction in integration cycle time; reused without rework across three major VW software releases.
Software release engineering & SDLC coordination
coordinated software content delivery across development, test, and project teams; managed Git branching strategies aligned to release milestones and SDLC phase exits; owned test case readiness, defect lifecycle status tracking, and quality gate compliance; reported integration health and release readiness to OEM programme stakeholders weekly.
Requirements & test traceability
maintained bidirectional traceability from safety goals through system requirements to test cases and validation evidence in DOORS and Jira Xray; automated coverage-matrix generation via Jira Xray REST API; zero untraced requirements at every programme milestone review.
Integration dashboards & observability
built CI/CD-integrated dashboards (GitLab, Python, Jira Xray) aggregating real-time integration health, quality KPIs, and test campaign status across multiple software builds; automated alerting on integration failures and quality-gate violations.
Defect triage & root-cause analysis
led structured triage of integration and regression failures with feature teams; isolated defects across ECU software, communication stacks, and test infrastructure; produced high-fidelity root-cause reports and drove corrective actions to closure; translated findings into concise summaries for programme management.
Cross-functional integration leadership & SDLC governance
chaired integration planning meetings, defect lifecycle review calls, and release decision gates across ADAS software, hardware, network, and test infrastructure teams; managed stakeholder escalations and drove conflict resolution for integration blockers; defined test plan entry / exit criteria and enforced release QA standards; mentored junior engineers in Git branching, CI/CD practices, and structured test processes.
HIL / SiL simulation & CARLA-based validation toolchain
designed closed-loop test sequences on HIL benches (dSPACE, Speedgoat) for ADAS ECU validation; built a complete CARLA 0.9.14 simulation toolchain (Python, Docker, AWS EC2 GPU) automating scenario execution, KPI evaluation, and Jira Xray REST API traceability updates; worked daily with CAN, LIN, and Ethernet at integration layer using Vector tools and Wireshark; managed ECU flash workflows and software update validation sequences.

IAV GmbH

Function Developer / Application Engineer

09/2021 – 03/2023

Gifhorn, Germany

Automated data integration tooling
built Python + Bash pipelines for .a2l / .hex ECU calibration file ingestion via INCA; automated feature extraction, sensor deviation correction, and structured database population (MySQL, InfluxDB) for time-series vehicle measurement data; zero manual steps after initial deployment.
Model-based development & validation
developed and validated Simulink / Stateflow models for vehicle air management systems; applied control theory, system identification, and statistical verification against CAN logged measurement data; maintained UML diagrams and DOORS style documentation for cross-team audit traceability; ensured model fidelity at SiL level before handover to HIL integration.
AI-driven controller optimisation
applied DDPG Reinforcement Learning to autonomously optimise controller parameters; achieved overshoot reduction from 15 % → 4 % over 50,000 training steps with formal convergence validation and documented performance evidence; implemented in Python / TensorFlow with structured test reports reviewed by engineering leadership.
Requirements engineering & measurement data analysis
decomposed vehicle system requirements into testable specifications; maintained traceability from system requirement to validation evidence; built pandas / asammdf data processing pipelines for multi-channel time-series analysis; produced structured reports for cross-functional review sessions including function owners and release management.

Fraunhofer IPA

Research Engineer — Simulation & Automation

07/2018 – 04/2019

Stuttgart, Germany

Simulation & automation tooling
developed simulation and automation tools for industrial manufacturing scenarios: cycle-time modelling, safety-zone analysis, and robot / sensor configuration evaluation; worked with multi-disciplinary teams across research and production-floor contexts.
Safety zone & risk assessment modelling
developed simulation models for safety zone definition in human-robot collaboration scenarios, applying risk assessment methodology (ISO 10218 / EN ISO 13849) to establish safe operational boundaries — directly analogous to ODD and MRC analysis used in autonomous driving functional safety.
Robot & sensor evaluation tool
engineered a Java application for systematic robot and sensor selection (cost, time, automation potential); designed SolidWorks 3D models of mechanical assemblies and produced manufacturing drawings and documentation to optimise production workflows and quality assurance.
Cross-functional research collaboration
integrated design, evaluation, and implementation phases within interdisciplinary teams spanning research and industrial automation; maintained project documentation on Confluence and managed version-controlled codebases with Git throughout the research lifecycle.

BMW Group / Mercedes-Benz Egypt

Automotive Diagnostics Trainee

2013 / 2014

Egypt

ECU diagnostics & safety verification
performed functional safety-system verification and ECU diagnostics on production-ready vehicles using OEM diagnostic platforms (CAN / LIN); interpreted DTC fault codes, live data, and test logs to distinguish hardware faults from software / configuration defects; supported final inspection and test-drive quality gates.
Automated test plan execution & sensor calibration
assisted technicians in executing systematic automated test plans using BMW diagnostic platforms; validated sensor calibration and verified the functionality of safety-critical and comfort systems across integrated electrical / electronic E / E architectures.
ECU software flashing & DTC fault-finding
applied OEM diagnostic tools over CAN / LIN to read and clear DTCs, perform control-unit tests, and execute guided troubleshooting; observed and supported ECU software update and flashing procedures, reinforcing understanding of software load validation and post-flash verification on commercial vehicles.

projects projekte

ADAS / CI/CD

One Pipeline, Three Releases — Zero Rework

Built a complete ADAS scenario validation toolchain from scratch at OEM programme level: CARLA 0.9.14 simulation engine, Python orchestration, Docker containerisation, AWS EC2 GPU fleet management, GitLab CI pipeline, and Jira Xray REST API integration for automated safety evidence generation. A single end-to-end pipeline from scenario execution to auditable traceability package — deployed unchanged across 3 consecutive software releases.

CARLA 0.9.14 Python Docker AWS EC2 GPU GitLab CI Jira Xray REST API

LLM / ML in Production

7× Faster: AI That Kills the Backlog

Integrated LLM-based requirements ambiguity detection and ML failure-mode classification into the live ADAS validation pipeline. Triage time cut from 20 min to 3 min (7× faster) — running in production on multi-platform software releases. Safety-critical anomaly flagging integrated into release decision gates.

Python LLM Integration ML Classifier asammdf / pandas REST API

Functional Safety

Zero Gaps: Every Safety Requirement Traced

Designed end-to-end ISO 26262 V&V workflows for AEB, LKA, ACC at Tier 1 OEM — ASIL-rated test plans, SOTIF / ISO 21448 ODD sweeps, bidirectional DOORS → Jira Xray traceability. Zero untraced requirements at every programme milestone.

ISO 26262 SOTIF DOORS Jira Xray

AI / Reinforcement Learning

Taught the ECU to Self-TuneECU zur Selbstoptimierung gebracht

Applied DDPG Reinforcement Learning to autonomously optimise PI controller parameters at IAV GmbH. Overshoot: 15 % → 4 % over 50,000 training steps. Formal convergence validation against CAN-logged vehicle measurement data.

Python DDPG TensorFlow CAN Analysis Simulink

DevOps / Observability

One Screen. Every Release. Zero Surprises.

Designed CI / CD-integrated monitoring dashboards for ADAS test campaign health in a Tier 1 OEM programme. Real-time KPI alerting for safety-critical anomalies, integrated with GitLab CI and Jira Xray. Full release-cycle visibility across multiple vehicle platforms in one view.

Python GitLab CI Jira Xray pandas matplotlib

key achievements wichtige erfolge

25%

Faster

End-to-End Integration Toolchain Built from Scratch

CARLA + Python + Bash + Docker + AWS + GitLab CI + Jira Xray — single automated pipeline from ECU software ingestion to signed release QA evidence. 25 % integration cycle time reduction across three major VW vehicle platforms; reused without rework across all releases.

3min

From 20 min

AI-Accelerated Defect Triage in Production

LLM-based requirements analysis + ML failure classifier deployed live in integration pipelines. Triage time per campaign reduced from 20 min → 3 min — a 7× acceleration enabling faster defect escalation and shorter release decision cycles.

Untraced Reqs.

Zero Untraced Requirements — Every Milestone

Full DOORS → Jira Xray bidirectional traceability from safety goals through system requirements to test cases and validation evidence — maintained at every programme milestone. Coverage-matrix generation automated via Jira Xray REST API.

Live
KPIs

Real-time

Fleet-Scale Integration Health Dashboards

Real-time CI/CD-integrated dashboards (GitLab + Python + Jira Xray) aggregating integration health, quality KPIs, and test campaign status across multiple simultaneous software builds and release branches. Automated alerting on quality-gate failures gave programme management instant visibility into integration risk — replacing manual status meetings.

SOTIF
ODD

ISO 21448

SOTIF Scenario Coverage Architecture SOTIF-Szenario-Coverage-Architektur

Designed multi-dimensional ODD parameter spaces for AEB, LKA, and ACC with statistical coverage evidence per ISO 21448 (SOTIF). Formal scenario coverage methodology that is directly applicable to AV / ADAS integration validation at scale — enabling argument-complete safety cases.

skills skills

ADAS Validation & Test Architecture

95%

Python / Bash Automation & Tooling

95%

CI/CD: GitLab, Docker, AWS

92%

Functional Safety: ISO 26262, SOTIF, ASPICE, UNECE WP.29

90%

ADAS Validation: HIL / SiL / MiL, CARLA

90%

Jira / Jira Xray / DOORS / Test Traceability

90%

AI / ML & LLM Integration

82%

Matlab / Simulink

85%

Tools & Standards Tools & Standards

ISO 26262 SOTIF / ISO 21448 ASPICE HARA / FMEA GitLab CI Docker AWS Python Bash Git Jira Xray DOORS CARLA HIL / SiL / MiL CAN / LIN / Ethernet Simulink INCA MySQL / InfluxDB DDPG / RL LLM Integration C / C++ TPT dSPACE / Speedgoat

education ausbildung

10/2020 – 03/2023

M.Sc Mechatronics & Robotics Engineering

Leibniz Universität Hannover

Hannover, Germany — Grade: 2.2

Mechatronic systems, robotics, and control theory — foundation for ADAS and embedded systems integration.

10/2019 – 10/2020

M.Eng Mechatronics

Hochschule Merseburg

Merseburg, Germany

Mechanical power engineering and mechatronics, bridging hardware and software perspectives.

09/2009 – 07/2014

B.Sc Mechanical Power Engineering

Mansoura University

Mansoura, Egypt

Solid fundamentals in mechanical engineering and energy systems — system-level thinking in automotive context.

certifications zertifikate

ISTQB® CTFL 4.0 — iSQI Group Prototype Driving Licence (Germany) Google Prompting Essentials — Coursera / Google Claude Code Architect — Anthropic In Progress

languages sprachen

German C1+ Professional

English C1+ Professional

Arabic Native

contact me kontakt

Senior ADAS & Systems Engineer — Validation, Functional Safety & Systems Integration

email

amro.mo.moustafa@gmail.com

phone telefon

+49 1590 1355023

location standort

Berlin, Germany

hi , i am AmroMoustafa. hi , ich binAmroMoustafa.

about me über mich

ADAS validation and systems integration engineer.

email

phone telefon

location standort

experience berufserfahrung

EDAG Group — embedded across Tier 1 OEM programmes CURRENT

IAV GmbH

Fraunhofer IPA

BMW Group / Mercedes-Benz Egypt

projects projekte

One Pipeline, Three Releases — Zero ReworkEine Pipeline, drei Releases — null Nacharbeit

7× Faster: AI That Kills the Backlog7× Schneller: KI eliminiert den Rückstand

Zero Gaps: Every Safety Requirement TracedNull Lücken: Alle Sicherheitsanforderungen rückverfolgbar

Taught the ECU to Self-TuneECU zur Selbstoptimierung gebracht

One Screen. Every Release. Zero Surprises.Ein Screen. Jedes Release. Null Überraschungen.

key achievements wichtige erfolge

End-to-End Integration Toolchain Built from Scratch End-to-End-Integrations-Toolchain von Grund auf aufgebaut

AI-Accelerated Defect Triage in Production KI-beschleunigte Defekt-Triage in der Produktion

Zero Untraced Requirements — Every Milestone Null ungetrackte Anforderungen — jeder Meilenstein

Fleet-Scale Integration Health Dashboards Flottenweite Integrations-Gesundheits-Dashboards

SOTIF Scenario Coverage Architecture SOTIF-Szenario-Coverage-Architektur

skills skills

95%

95%

92%

90%

90%

90%

82%

85%

education ausbildung

10/2020 – 03/2023

M.Sc Mechatronics & Robotics Engineering

Leibniz Universität Hannover

Hannover, Germany — Grade: 2.2

10/2019 – 10/2020

M.Eng Mechatronics

Hochschule Merseburg

Merseburg, Germany

09/2009 – 07/2014

B.Sc Mechanical Power Engineering

Mansoura University

Mansoura, Egypt

certifications zertifikate

languages sprachen

contact me kontakt

Amro Moustafa

email

phone telefon

location standort

hi , i am
Amro
Moustafa. hi , ich bin
Amro
Moustafa.

One Pipeline, Three Releases — Zero Rework

7× Faster: AI That Kills the Backlog

Zero Gaps: Every Safety Requirement Traced

One Screen. Every Release. Zero Surprises.

End-to-End Integration Toolchain Built from Scratch

AI-Accelerated Defect Triage in Production

Zero Untraced Requirements — Every Milestone

Fleet-Scale Integration Health Dashboards