Enis BILICAN

Embedded Software & Critical Systems Engineer

EMBEDDED SOFTWARE AND CRITICAL SYSTEMS ENGINEER, FN Herstal, 01/2023 TO 10/2023, 04/2025 TO NOW

Context of the project

FN Herstal is a well-known defense and security company. I worked on the software and integration of a complex defense system involving embedded software, an HMI, cameras, communication layers and electromechanical subsystems. The work required close collaboration with system engineers, developers, QA and validation teams.

Achievements

I contributed to the software design, C++ development, system architecture, SCADE models, HMI implementation and embedded Linux integration. I also supported the integration of software with the electromechanical systems and communication layers.

Details

✓ Created the main HMI proof of concept in C++ with Qt and contributed to the final HMI implementation with Ansys SCADE Display.

✓ Developed confidential C++ and Python algorithms for system configuration, communication layers and AI-assisted system functions.

✓ Implemented and supported communication interfaces including CAN and TCP/IP between software components and hardware subsystems.

✓ Worked on embedded Linux integration using Yocto and contributed to hardware/software deployment activities.

✓ Co-developed AI-assisted system functions by combining embedded software, camera-based data, configuration algorithms and hardware/software integration constraints.

✓ Contributed to software detailed design, API understanding, functional validation and technical documentation.

✓ Supported QA and validation engineers during User Acceptance Testing, troubleshooting and validation activities.

✓ Coached developers through code review, best practices and system-level understanding.

Team

8 embedded software engineers, 2 QA engineers and a PM

Technical and methodology

C++, native libraries, Boost, Qt, Python, OpenCV, PyQt5, TensorFlow, Ansys SCADE Suite, SCADE Display, Yocto Linux, CAN, TCP/IP, embedded hardware integration, Scrum methodology.

Trainer in Digital Technologies

Training designer and instructor in digital technologies, HSO Geneve, Otofacto, GSK, 01/2021 TO NOW

Context of the project

I design and deliver practical training for engineering, operations and business teams. The sessions cover digital tools, AI, industrial vision and everyday productivity, with examples that participants can use in their own work.

Achievements

I have helped several organisations adopt AI analysis tools, industrial vision systems and more efficient workflows. I train engineers, project managers and administrative teams on ChatGPT, Copilot, Microsoft 365 and practical automation.

Details

✓ Industrial vision: trained 25 managers, engineers and quality leads in the fundamentals, algorithm tuning and troubleshooting.

✓ Visual defect detection: coached 12 pharmaceutical engineers on ML model development, testing and deployment for quality control.

✓ Trained HR, administrative and operations teams to use ChatGPT and Copilot for documentation, data analysis and routine tasks.

✓ Project management and Microsoft 365: trained engineers in planning, follow-up, collaboration and Copilot.

✓ AI-enabled machines: delivered a three-day international training for 21 engineers and regulatory managers on integration, IT constraints, compliance and deployment.

Team

Engineers, project managers, specialists, technical teams, HR professionals and administrative staff from various sectors including manufacturing, IT, defense, pharmaceuticals and higher education.

Technical and Methodology

Industrial vision, market analysis, production-line implementation, Python, TensorFlow, ChatGPT, Copilot, Excel, VBA, Project, Teams, Word, PowerPoint and Outlook. Workshops, coaching and practical case studies.

Automatic Visual Inspection (AVI)

AI PROJECT ENGINEER & WORKSTREAM LEAD, Takeda, 04/2024 TO 04/2025

Context of the project

I worked on the implementation of an Automatic Visual Inspection system for several biological product formats. The system included 17 camera stations, robotic handling, GPU processing, vision equipment and industrial IT in a regulated pharmaceutical environment.

Achievements

I led the AI, IT and vision workstream, from requirements and specifications to supplier coordination and project follow-up. I coordinated the integration of the 17 camera stations, organised technical workshops and supplier reviews, and followed the AI, data, GPU, validation and compliance requirements.

Details

✓ Led the AI, IT and Vision workstream for the deployment of an Automatic Visual Inspection system across 17 camera stations with robotic integration.

✓ Managed the upstream project phase, including requirements collection, technical scope definition, specification writing, supplier clarification and project execution follow-up.

✓ Reviewed and improved supplier vision algorithms through threshold calibration, false-positive reduction and defect detection sensitivity analysis.

✓ Managed the sizing and validation of GPU-based processing infrastructure for real-time AI inference and parallel image processing.

✓ Defined IT integration requirements including data retention, storage strategy, network routing and interoperability with manufacturing systems.

✓ Led the definition of defect taxonomy, inspection performance criteria and AI model evaluation logic.

✓ Coordinated workshops with production, QA, engineering teams, global management and external vendors.

✓ Reviewed supplier technical proposals, challenged design choices and followed up technical actions with machine vendors.

✓ Contributed to inspection method standardization and validation strategy in alignment with pharmaceutical quality requirements (USP <1790>).

Team

1 Project manager, 1 team manager, 4 process engineers, 1 IT infrastructure manager, 1 Computer systems manager, 2 QA, 2 QV.

Technical and methodology

MS Project, Python, PyTorch, computer vision, AI-based visual inspection, GPU processing infrastructure, Digital Twin Visual Components 4.6, Brevetti Vision configuration, Prometheus digital twin, pharmaceutical validation, USP <1790>.

Real-Time Image Processing & LiDAR Engineer

EMBEDDED SOFTWARE ENGINEER, REAL-TIME IMAGE PROCESSING & LIDAR, Arcelor Mittal, 10/2023 TO 04/2024

Context of the project

I developed machine-vision and LiDAR solutions for a metallurgy site, with a focus on product quality, operator safety and production efficiency. The systems inspected large products directly on the production lines and helped improve measurement reliability.

Achievements

I developed real-time image-processing and LiDAR software using existing 2D cameras to inspect shape and dimensions from five metres away. I also built RTSP streaming for the plant HMIs and integrated a LiDAR system to compare its measurements with the camera results.

Details

✓ Developed a real-time low-level RTSP video streaming server in C with GStreamer and FFmpeg to securely stream high-resolution industrial images up to 22000×1080 pixels.

✓ Built Python algorithms for straightness measurement, object detection, data merging, relative and absolute error calculation, image stitching and dynamic camera calibration.

✓ Developed AI feature-extraction logic from 2D image data, enabling advanced analysis of product shape, dimensional deviations and background stabilization.

✓ Integrated LiDAR data with 2D camera measurements to improve product shape detection, thickness estimation and quality-control precision.

✓ Developed C++ 3D object detection logic based on LiDAR data preprocessing and industrial sensor integration.

✓ Improved measurement reliability by comparing camera-based and LiDAR-based results and calculating relative errors.

Team

2 embedded software engineers and the R&D technical leader.

Technical and methodology

C with GStreamer and FFmpeg (RTSP server), C++ (3D object detection), Python, OpenCV, Boost, Git. LiDAR drivers, RTSP streaming, sensor fusion, real-time image processing.

Pharmaceutical machine vision

PROCESS AND MACHINE VISION ENGINEER, GSK (GlaxoSmithKline), 03/2022 TO 12/2022

Context of the project

At GSK, I worked in the Filling and Visual Inspection department on systems for syringes and vials. The goal was to reduce false rejects without lowering defect detection performance. The scope covered particles, foreign objects, cracks, scratches and other glass defects across more than 50 programs and four production lines.

Achievements

I developed, optimised and validated vision algorithms across four production lines. I adjusted lighting, cameras and inspection parameters, handled troubleshooting and helped launch a Pharma 4.0 initiative to standardise algorithms that had been developed independently over many years.

Details

✓ Optimized lighting systems, camera settings and inspection thresholds to reduce false rejects while maintaining high defect detection performance.

✓ Ensured reliable operation of automatic visual inspection equipment through troubleshooting, corrective actions and preventive maintenance.

✓ Led the standardization of machine vision algorithms by comparing historical detection methods, identifying best practices and promoting harmonized inspection strategies across products and production lines.

✓ Conducted production performance monitoring through KPI analysis, trend evaluation and continuous improvement initiatives.

✓ Led deviation investigations and Problem Solving Sessions (PSS) using Six Sigma methodologies, 5 Whys analysis and Root Cause Analysis.

✓ Participated in continuous improvement projects including technical analysis, business case preparation, planning and implementation.

✓ Contributed to validation and qualification activities (IQ, OQ, PQ) and GMP-compliant documentation.

Team

5 engineers in process and vision, 2 QA and 3 technicians.

Technical and methodology

Seidenader machine vision programming, ImageJ, VBA, Python, VMware, Six Sigma, 5 Whys, Root Cause Analysis, GMP, IQ/OQ/PQ, KPI monitoring.

LiDAR Integration for Autonomous Driving R&D

DATA POST PROCESSING ENGINEER, AGC, 10/2021 TO 02/2022

Context of the project

This R&D project focused on integrating LiDAR sensors behind automotive windshields. I processed and standardised 3D point-cloud data to evaluate optical performance and study sensor behaviour. Accurate environment modelling was an important step towards autonomous driving.

Achievements

I developed the reception, parsing and post-processing of LiDAR data, with an emphasis on robust, near real-time processing. I integrated several sensors over TCP/IP and contributed to the analysis of 3D point clouds for object characterisation and sensor evaluation.

Details

✓ Developed LiDAR data reception, parsing and standardization algorithms for automotive sensor R&D.

✓ Built a robust TCP/IP communication and data-processing workflow to integrate multiple LiDAR sensors into AGC analysis protocols.

✓ Implemented self-diagnostic logic to improve reliability in test conditions where live debugging was limited.

✓ Created dynamic parsing procedures for point-cloud data extraction, standardized data storage and near real-time analysis.

✓ Developed mathematical processing algorithms for 3D point-cloud analysis, including spatial transformations, distance calculations and geometric feature extraction.

✓ Supported AI and deep learning experimentation on 3D LiDAR data for object characterization and sensor performance evaluation.

✓ Tested the algorithm under real conditions with different windshield glass configurations.

✓ Supported deep learning hyperparameter optimization for different windshield materials and sensor configurations.

Team

Multidisciplinary 6 R&D engineers (optical, mechanical, electronic and IT)

Technical and methodology

C# with .NET Framework, Python with TensorFlow, Wireshark for frame analysis, TCP/IP communication, LiDAR software, 3D point-cloud processing, geometric analysis, spatial mathematics, deep learning, near real-time parsing.

Industry 4.0 Vision Solution for Food Manufacturing

INDUSTRIAL IT SYSTEMS ENGINEER, PASQUIER, 09/2020 TO 10/2021

Context of the project

I was part of the Methods, Projects and Industrial IT department. I developed a mobile vision prototype for product traceability and sorting, using real-time volume and colour checks with feedback to the information system. I handled the project from specifications to architecture, algorithms, electronics and HMI.

Achievements

I translated production and quality needs into functional specifications, selected the sensor architecture and developed the volume and colour algorithms. The result was a working mobile prototype that could be moved between lines and tested with different product families.

Details

✓ Led the design and deployment of an Industry 4.0 machine-vision solution, proposing technical improvements and alternative architectures for product traceability, sorting and quality control.

✓ Collaborated with Quality Assurance and Mechanical Design teams to translate operational needs into functional specifications and select the most suitable vision-system architecture.

✓ Developed real-time Python/C++ algorithms for 3D volumetric measurement using triangulation sensors, including point-cloud processing, dynamic adaptation to multiple product formats and reliability monitoring.

✓ Designed advanced color-analysis algorithms based on the CIELAB color space and DeltaE metrics to assess product cooking consistency, while optimizing lighting and environmental conditions.

✓ Implemented data formatting and communication workflows to exchange information between software processes and industrial databases.

✓ Designed and built a mobile industrial prototype integrating 3D sensors, vision hardware, processing algorithms, .NET-based HMI, electrical systems and custom mechanical housing.

✓ Implemented automated calibration procedures and delivered training sessions to technicians, enabling efficient adoption and long-term operation on production lines.

Team

Operational and Industrial IT department

Technical and methodology

Python with NumPy, OpenCV, Colour-Science modules, C++, ImageJ, .NET HMI, 3D sensor SDK, database communication, AutoCAD electrical design.

Connected and ventilated face mask

ELECTRONICS AND EMBEDDED ENGINEER, 10/2020 TO 04/2021

Context of the project

I developed a connected, ventilated mask prototype as my end-of-study project. It combined embedded control, airflow regulation, BLE communication and an Android application.

Achievements

I built a functional prototype with a 3D-printed structure, Arduino control, PWM fan regulation, differential pressure sensing and BLE communication with a native Android application.

Details

✓ Conducted technology benchmarking and market analysis to identify existing solutions and evaluate public-market constraints.

✓ Developed an embedded control algorithm using a differential pressure sensor to compare internal and external mask pressure and dynamically adjust fan speed as well as opening and closing cycles, avoiding imposed breathing rhythms.

✓ Optimized comfort and autonomy through energy-management strategies, battery sizing and mass-distribution analysis.

✓ Designed the mechanical structure in Autodesk Inventor for 3D printing, ensuring flexibility, user comfort and cable routing through a dedicated 'spider-web' architecture.

✓ Designed the electronic architecture around an Arduino Nano platform, selecting and sizing components for safe operation, integrating protection resistors and capacitors to minimize electrical disturbances.

✓ Implemented Bluetooth Low Energy (BLE) communication between the embedded system and an Android application for configuration and monitoring.

✓ Developed Android application features allowing users to configure ventilation modes, switch between automatic and manual fan-speed control, and visualize operational statistics through dashboards.

Team

Binomial with another IT Engineer

Technical and methodology

Arduino C, BLE communication, Android Studio, Java, XML, PWM control, differential pressure sensor, Autodesk Inventor, 3D printing, Fritzing, electromagnetic disturbance management.

Connected smart home

IT ENGINEER, 10/2019 TO 06/2020

Context of the project

I designed and developed a connected-home system controlled from a mobile application. The project compared several industrial and consumer communication protocols and focused on making different devices work together.

Achievements

I designed the system architecture and connected embedded controllers, wireless protocols and mobile applications. I tested several protocols to find a reliable way to communicate with the different devices.

Details

✓ Developed a cross-platform mobile application to monitor and control connected devices from a centralized interface.

✓ Implemented BLE communication between embedded devices and the mobile application for local control and configuration.

✓ Integrated Z-Wave technology through a gateway architecture to manage smart lighting systems.

✓ Developed REST API interactions to exchange data between connected devices, gateways and external services.

✓ Implemented LoRaWAN connectivity for long-range, low-power communication scenarios.

✓ Configured MQTT-based messaging to enable event-driven communication and real-time device status updates.

✓ Designed and tested end-to-end communication workflows involving multiple protocols and heterogeneous devices.

Team

Solo development

Technical and methodology

Arduino C, WinDev, BLE, Z-Wave (REST API), LoRaWAN, MQTT, mobile application development, embedded systems, IoT architecture.

Connectivity for IOT sensors

DATA AND IOT ENGINEER, 10/2019 TO 01/2020

Context of the project

I developed an Industrial IoT platform to collect, process, store and display environmental data. The project used both a server-client connection and message-based communication, then sent the same data to two different IIoT platforms to check its portability.

Achievements

I built the data flow from sensor acquisition and preprocessing to database storage and dashboards. The main focus was reliable data, live monitoring and easy integration with other tools.

Details

✓ Integrated temperature and humidity sensors with a Raspberry Pi platform to create a low-cost industrial monitoring solution.

✓ Developed Python data-processing pipelines using NumPy and Pandas for data cleansing, aggregation and formatting.

✓ Implemented MQTT and REST-based communication services to support reliable data collection and transmission.

✓ Designed a time-series storage architecture using InfluxDB for historical analysis, trend monitoring and data retention.

✓ Created interactive dashboards with Dash to visualize live measurements, system status and configuration parameters.

✓ Implemented automated data acquisition and logging mechanisms to reduce manual intervention and improve traceability.

✓ Validated end-to-end data flows from sensor acquisition to visualization and reporting.

Team

Solo development

Technical and methodology

Python with NumPy, Pandas, Matplotlib, Dash modules, REST API, MQTT, InfluxDB, Raspberry Pi, time-series databases, data pipelines, sensor integration, Fritzing.

This website, Flask on a Raspberry Pi

SOFTWARE AND WEB SERVER ENGINEER, 06/2023 TO NOW

Project overview

I built and maintain this portfolio on a Raspberry Pi running Ubuntu Server 22.04 LTS. It presents my work, experience, studies and freelance services. Apache handles the web server, Flask handles the application, and a file-based translation system provides the English and French versions.

What I built

The site includes session management, a contact form, language switching and QR code generation without storing the generated codes on the server. APScheduler removes old temporary files so the small server stays tidy.

Details

✓ Hardware and software: Raspberry Pi running Ubuntu Server 22.04 LTS.

✓ Server: port forwarding, reverse proxy, SSL, domain management with Porkbun and Apache.

✓ Backend: Flask application with session and data management.

✓ QR code: real-time generation and display without server-side storage. Try www.enis.pro/qr-code.

✓ Languages: file-based translations for English and French.

✓ Maintenance: APScheduler removes outdated temporary files.

Team

Solo development

Technical stack

Python, Flask, Flask-Mail, APScheduler, LoggerManager, Apache, Raspberry Pi and Docker