AI Engineer & Full Stack DeveloperEngineer
Passionate about crafting exceptional digital experiences with clean code, scalable architecture, and innovative solutions
About Me
Building intelligent systems that combine solid engineering with practical AI
Full Stack Engineer & Applied AI Researcher.
I’m a software engineer and AI practitioner with hands on experience across backend systems, mobile and web development, and modern machine learning workflows. My work spans Java Spring services, cross platform apps, database design, and the integration of NLP and computer vision models into real applications. Currently pursuing a Master’s in Data Science & AI, I focus on benchmarking LLM finetuning techniques, building reproducible ML pipelines, and designing scalable architectures that bridge traditional engineering with advanced AI capabilities. I enjoy transforming complex ideas into practical, reliable systems whether it’s deploying full stack applications, experimenting with deep learning models, or crafting intelligent tools that automate real world tasks.
Professional Experience
Hands on engineering across AI/ML and full stack development, contributing to production systems and research driven prototypes.
AI & Full Stack Engineer
encoway GmbH
•GermanyContributed as a team member in both the AI/ML and Full Stack engineering teams, working on LLM based architectures, RAG systems, model training, and scalable application development.
Key Achievements:
- Implemented LLM based pipelines including RAG architectures, embedding generation, and retrieval optimization
- Fine tuned pretrained transformer models for domain specific tasks such as classification, extraction, and summarization
- Developed and maintained computer vision workflows including detection, segmentation, and document understanding
- Built backend services and APIs supporting AI inference, data processing, and multi platform applications
- Improved annotation and preprocessing pipelines by resolving scaling, coordinate, and mask alignment issues
- Collaborated with cross functional teams to integrate AI features into production ready web and mobile applications
Technologies Used:
Full Stack & Mobile Developer
personal experience
•GermanyWorked as part of the full stack engineering team building scalable backend systems, mobile apps, and internal tools.
Key Achievements:
- Developed mobile applications using React Native with authentication, notifications, and cloud integration
- Implemented backend services and REST APIs powering multi domain applications
- Contributed to CI/CD pipelines improving deployment reliability and developer workflow
- Delivered multilingual, accessible, and high performance user interfaces
- Collaborated with designers and product teams to translate requirements into maintainable engineering solutions
Technologies Used:
Software Engineer Intern
Industry Internship (Bremen/Germany) encoway GmbH
•GermanyWorked as a full stack engineer with strong involvement in backend, frontend, and database development using Java Spring and Spring Boot. Contributed to production‑grade software systems, integrated NLP models into applications, and collaborated using agile workflows with GitHub, Jira, and code reviews.
Key Achievements:
- Developed backend services and REST APIs using Java Spring Boot with secure authentication and role based access
- Implemented frontend components and UI workflows integrated with backend endpoints
- Designed and optimized relational database schemas and queries for production use
- Integrated NLP models into software systems to extract structured information from unstructured text (e.g., auto filling form fields from PDF text)
- Built document processing pipelines including text extraction, entity recognition, and automated data mapping
- Collaborated in an agile team using Jira boards, GitHub pull requests, and structured code reviews
- Debugged and improved system reliability through logging, testing, and performance analysis
Technologies Used:
Skills & Expertise
A versatile engineering toolkit spanning AI, machine learning, data science, full‑stack development, and mobile applications.
AI Engineering
Building intelligent systems using modern LLM architectures, retrieval pipelines, and production‑ready AI integrations.
Machine Learning & Data Science
Designing, training, and evaluating ML models with strong focus on computer vision, preprocessing, and data‑centric workflows.
Full Stack Development
Building scalable, maintainable applications across web and backend systems with modern frameworks and clean architecture.
Mobile Development & Personal Projects
Delivering cross platform mobile experiences and building real world community apps with multilingual capabilities.
Backend & Cloud
Developing reliable backend systems, APIs, and cloud‑ready services for production environments.
DevOps & Tooling
Ensuring smooth development workflows, automation, and reliable deployments across environments.
Additional Expertise
Featured Projects
Showcasing innovative solutions that demonstrate technical expertise and creative problem solving
Predictive Analytics Dashboard
An application in R for predictive modeling. Includes multiple ML models like Random Forest, XGBoost and uploaded image analysis.
Key Features:
Tech Stack:
Christian Church App, Community Platform
A full stack mobile and web application for church community management. Built with Expo and Supabase, featuring multi provider authentication.
Key Features:
Tech Stack:
RAG Benchmarking Vector vs Graph RAG
Bachelor thesis project evaluating and benchmarking information retrieval performance between Vector based RAG and Graph based RAG pipelines.
Key Features:
Tech Stack:
LLM Finetuning Benchmarking
Master thesis project developing a benchmarking experiment to compare multiple LLM finetuning techniques, includes PEFT, instruction tuning etc.
Key Features:
Tech Stack:
Multi Domain ML Model Training Suite
A comprehensive ML repository containing modular implementations of image, text classification, object detection, segmentation, and OCR models.
Key Features:
Tech Stack:
Kaggle ML Experiments
A collection of GPU accelerated ML experiments developed on Kaggle, covering image, text classification, object detection, segmentation, OCR.
Key Features:
Tech Stack:
Education & Learning
Continuous learning and academic excellence in computer science and software engineering
Bachelor of Science in Computer Engineering
Hochschule Bremen University of Applied Sciences
•Bremen, GermanyAn engineering program combining computer science, embedded systems, backend development, databases, and applied AI. Bachelor thesis focused on benchmarking information retrieval performance between vector based RAG and Graph RAG architectures.
Relevant Coursework:
- Algorithms & Data Structures
- Database Systems & SQL Optimization
- Backend Development & API Engineering
- Frontend Engineering (Web & Mobile Basics)
- Operating Systems & Computer Architecture
- Embedded Systems & Microcontroller Programming
- Networks & Distributed Systems
- Software Engineering & System Design
- Mathematics for Computer Science
- Introduction to Machine Learning
- Deep Learning Concepts and model training
- Computer Vision & Image Processing
- Natural Language Processing
- Project Management & Agile Methodologies
Achievements:
- Completed multiple full stack and backend focused academic projects
- Developed embedded and hardware adjacent applications in C
- Built ML/AI prototypes as part of coursework
Master of Science in Data Science & Artificial Intelligence
Hochschule Stralsund University of Applied Sciences
•Stralsund, GermanyA program covering machine learning, deep learning, statistical modeling, data engineering. Emphasis on practical experimentation, reproducible research, and scalable ML/AI deployment. Master's thesis focuses on benchmarking and evaluating different LLM finetuning techniques.
Relevant Coursework:
- Machine Learning & Statistical Modeling
- Deep Learning Architectures & Optimization
- Computational Statistics
- Data Engineering & Big Data Processing
- Natural Language Processing
- Information Retrieval & Recommender Systems
- Cloud Computing for AI Workloads
- MLOps & Model Deployment
- Computer Vision & Image Processing
- Ethics of AI & Responsible Machine Learning
Achievements:
- Designed and evaluated multiple LLM finetuning pipelines (PEFT, RL based alignment, instruction tuning)
- Built multiple applied ML projects across NLP, CV, and data engineering domains
- Designed pipelines for model evaluation and dataset processing
Certificates & Awards
Certifications and recognition demonstrating expertise across various technologies and methodologies
Google Cloud Generative AI Fundamentals
Google Cloud Skills Boost
Completed foundational training in generative AI, large language models, and responsible AI principles.
Skills Covered:
Microsoft AI Fundamentals Learning Path
Microsoft Learn
Completed Microsoft Learn training covering machine learning concepts, Azure AI services, and responsible AI.
Skills Covered:
Kaggle Titanic ML Competition Participant
Kaggle
Participated in the Titanic ML competition, experimenting with feature engineering and classical ML models.
Skills Covered:
Kaggle Dataset
Kaggle
Published the personal dataset for experimentation with different task based model training.
Skills Covered:
Let's Work Together
Ready to bring your ideas to life? I'm always excited to discuss new opportunities, innovative projects, and potential collaborations.
Get In Touch
Whether you have a project in mind, need technical consultation, or just want to connect, I'd love to hear from you. Let's discuss how we can create something amazing together.
nathan.kadisoh@gmail.com
Response within 24 hours
Phone
+49 17636466945
Available Mon-Fri, 9AM-6PM CET
Location
Stralsund, Mecklenburg Vorpommern
Open to remote opportunities worldwide
Send Me a Message
I'll get back to you within 24 hours. Looking forward to hearing from you!