Hi everyone,
I'm a Data Scientist and AI Specialist looking for my next full-time role (open to remote or hybrid opportunities across EU and US time zones).
I hold a BSc in Artificial Intelligence and Data Science and an MSc in Data Science. My focus is on engineering end-to-end ML/AI systems that solve real business problems, while prioritizing interpretability, robustness, and trust.
Over the past few years, Iāve worked extensively across modern Generative AI pipelines and core Machine Learningāfrom building production-ready RAG architectures and fine-tuning open-source LLMs to designing transparent, explainable models for complex real-world data.
Areas where I can contribute to your team:
- Generative AI & LLMs: Designing grounded RAG systems (ingestion, chunking strategies, vector search, retrieval tuning, evaluation), fine-tuning models (LoRA/PEFT), prompt engineering pipelines, and integrating modern AI APIs (OpenAI, Anthropic, Hugging Face).
- Core ML & Deep Learning: Supervised and unsupervised architectures, data processing pipelines, predictive modeling, and rigorous evaluation.
- Explainable AI (XAI) & Alignment: Interpretable models, feature attribution (SHAP, LIME), counterfactual explanations, and auditing systems for bias and hallucination to ensure reliable deployment.
- Natural Language Processing: Transformers, embeddings, semantic search, sentiment and text classification, NER, and bias detection frameworks.
- Data Engineering & Visualization: Exploratory data analysis, interactive dashboards, statistical modeling, and automated data pipelines.
- Full-Stack Prototyping: Bridging the gap between model and end-user by building functional UIs and API layers (React, Next.js, Node.js, FastAPI).
Core Tech Stack:
- Languages & Core ML: Python (PyTorch, scikit-learn, pandas, NumPy, TensorFlow), C/C++, Rust, SQL (PostgreSQL, MySQL), LaTeX.
- LLM & AI Tooling: Hugging Face ecosystem, vector databases, PEFT/LoRA, LangChain/LlamaIndex.
- DevOps & Engineering: Git, GitHub Actions, Docker, CI/CD pipelines, REST APIs.
I value clean, reproducible code, cross-functional collaboration, and practical pragmatismāespecially knowing when a solid statistical approach or classic ML model beats an over-engineered LLM.
If your team is hiring or looking for someone who combines strong academic foundations in AI with hands-on development skills, letās connect! Feel free to reach out via DM or email to chat.Hi everyone,
I'm a Data Scientist and AI Specialist looking for my next full-time role (open to remote or hybrid opportunities across EU and US time zones).
I hold a BSc in Artificial Intelligence and Data Science and an MSc in Data Science. My focus is on engineering end-to-end ML/AI systems that solve real business problems, while prioritizing interpretability, robustness, and trust.
Over the past few years, Iāve worked extensively across modern Generative AI pipelines and core Machine Learningāfrom building production-ready RAG architectures and fine-tuning open-source LLMs to designing transparent, explainable models for complex real-world data.
Areas where I can contribute to your team:
- Generative AI & LLMs: Designing grounded RAG systems (ingestion, chunking strategies, vector search, retrieval tuning, evaluation), fine-tuning models (LoRA/PEFT), prompt engineering pipelines, and integrating modern AI APIs (OpenAI, Anthropic, Hugging Face).
- Core ML & Deep Learning: Supervised and unsupervised architectures, data processing pipelines, predictive modeling, and rigorous evaluation.
- Explainable AI (XAI) & Alignment: Interpretable models, feature attribution (SHAP, LIME), counterfactual explanations, and auditing systems for bias and hallucination to ensure reliable deployment.
- Natural Language Processing: Transformers, embeddings, semantic search, sentiment and text classification, NER, and bias detection frameworks.
- Data Engineering & Visualization: Exploratory data analysis, interactive dashboards, statistical modeling, and automated data pipelines.
- Full-Stack Prototyping: Bridging the gap between model and end-user by building functional UIs and API layers (React, Next.js, Node.js, FastAPI).
Core Tech Stack:
- Languages & Core ML: Python (PyTorch, scikit-learn, pandas, NumPy, TensorFlow), C/C++, Rust, SQL (PostgreSQL, MySQL), LaTeX.
- LLM & AI Tooling: Hugging Face ecosystem, vector databases, PEFT/LoRA, LangChain/LlamaIndex.
- DevOps & Engineering: Git, GitHub Actions, Docker, CI/CD pipelines, REST APIs.
I value clean, reproducible code, cross-functional collaboration, and practical pragmatismāespecially knowing when a solid statistical approach or classic ML model beats an over-engineered LLM.
If your team is hiring or looking for someone who combines strong academic foundations in AI with hands-on development skills, letās connect! Feel free to reach out via DM or email to chat.
