08/03/2026 updated
100 % available
AI/ML Researcher & Software Engineer
Karlskrona, Sweden
Worldwide
M.Sc. in Computer Science (expected Fall 2026), Blekinge Institute of TechnologyAbout me
An AI/ML-focused Computer Science researcher specializing in machine learning architectures, generative AI, and scalable cloud topologies. Demonstrated expertise in orchestrating comprehensive empirical evaluations, engineering privacy-preserving AI frameworks, and optimizing predictive trust.
AI AgentsLLMApplication DesignMobile Application DevelopmentCloud ComputingCyber Security StandardsSoftware ArchitectureProject Management Life CycleSoftware Project Management
AI & Machine Learning
Expertise in machine learning architectures, deep learning, TensorFlow, Scikit-learn, PyTorch, Neural Networks, and Explainable AI (XAI), including the development of privacy-preserving federated learning frameworks and supervised machine learning deployments.
Python & Programming Languages
Proficiency in Python, C++, Java, and SQL, with hands-on application in developing predictive analytics tools, data processing pipelines, and machine learning models.
Software Engineering & Cloud
Knowledge of Software Engineering principles, OOP, Git, GitHub, RESTful APIs, FastAPI, and cloud services including AWS (EC2, RDS, S3), applied to building scalable and enterprise-grade systems.
Generative AI & Constitutional AI
Focus on generative AI and constitutional AI as part of advanced academic research and specialization at Blekinge Institute of Technology.
Feature Engineering & Data Preprocessing
Application of feature engineering and preprocessing techniques to improve prediction accuracy and optimize data pipelines for decision support systems.
Research Methods & Performance Benchmarking
Competence in experimental design, performance benchmarking, and predictive analytics, including comparative analysis of algorithms such as RIPPER and CN2 for financial risk assessment.
Explainability & Model Evaluation
Use of SHAP-based explainability and probability calibration to compare and validate machine learning and deep learning models against established benchmarks.
Data Security & Compliance
Collaboration with cross-functional teams to fortify data security protocols and ensure compliance standards in AI engineering projects.
Expertise in machine learning architectures, deep learning, TensorFlow, Scikit-learn, PyTorch, Neural Networks, and Explainable AI (XAI), including the development of privacy-preserving federated learning frameworks and supervised machine learning deployments.
Python & Programming Languages
Proficiency in Python, C++, Java, and SQL, with hands-on application in developing predictive analytics tools, data processing pipelines, and machine learning models.
Software Engineering & Cloud
Knowledge of Software Engineering principles, OOP, Git, GitHub, RESTful APIs, FastAPI, and cloud services including AWS (EC2, RDS, S3), applied to building scalable and enterprise-grade systems.
Generative AI & Constitutional AI
Focus on generative AI and constitutional AI as part of advanced academic research and specialization at Blekinge Institute of Technology.
Feature Engineering & Data Preprocessing
Application of feature engineering and preprocessing techniques to improve prediction accuracy and optimize data pipelines for decision support systems.
Research Methods & Performance Benchmarking
Competence in experimental design, performance benchmarking, and predictive analytics, including comparative analysis of algorithms such as RIPPER and CN2 for financial risk assessment.
Explainability & Model Evaluation
Use of SHAP-based explainability and probability calibration to compare and validate machine learning and deep learning models against established benchmarks.
Data Security & Compliance
Collaboration with cross-functional teams to fortify data security protocols and ensure compliance standards in AI engineering projects.
Experience
Professional experience0 - 5 years
Freelancer since2026
Languages
EnglishFluentSwedishGood
Project history
Developed Python applications for predictive analytics and intelligent data processing. Built machine learning models using Scikit-learn and evaluated model performance. Applied feature engineering and preprocessing techniques to improve prediction accuracy. Collaborated with mentors to document technical implementation and project outcomes.
Developed a privacy-preserving federated learning framework for diabetes prediction. Compared machine learning and deep learning models with probability calibration and SHAP-based explainability. Tech Stack: Python, Scikit-learn, TensorFlow, SHAP, Pandas, NumPy.
Conducted comparative analysis of RIPPER and CN2 algorithms for financial risk assessment, evaluating accuracy, precision, recall, and F1-score. Demonstrated that CN2 outperformed RIPPER with an accuracy rate of 47.51% against 38.24%.
Certificates
Solution Architect
SAP2026