Professional Overview
AI Engineer specializing in LLMs, fine-tuning & RAG. Contributor to the Unsloth finetuning framework (60k users), led the Unsloth Studio beta to 400k downloads. At GravityQ I shipped production AI: Agentic, RAG, embeddings, prompt engineering, containerization, and BPMN workflow automation, with Python, transformers, and cloud infra (Azure, GCP)
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Core contributor to the Unsloth fine-tuning framework; built core features.
- Led the team that shipped Unsloth Studio beta to 400k downloads in 2.5 months.
- Implemented LoRA/QLoRA and embedding fine-tuning; added new model architectures.
- Resolved complex dependency chains; dockerized a stable release of the library.
Key Achievements / KPIs
- Led the team that delivered the Studio beta (400k downloads) release end to end.
- Shipped and maintained a stable dockerized release of the library, reaching 160k
- Built the majority of the python Unsloth Studio backend API.
- Implemented and maintained features in core Unsloth library (60k users).
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built production AI systems for clients using Python, transformers, and RAG.
- Designed agentic pipelines turning unstructured data into BPMN 2.0 workflows.
- Built and deployed scalable MLOps infrastructure on Azure and GCP.
- Applied LLMs, embeddings, topic modeling, and classification to client data.
- Deployed microservices and streaming prediction services for live inference.
Key Achievements / KPIs
- Raised a client's model accuracy from 40-50% to 80-85%.
- Delivered scalable MLOps infrastructure enabling reliable production inference.
- Partnered with a client over 2 years across technical and product strategy.
Education
Field of Study: Datascience and Statistics
Field of Study: Commercial Mediation
Field of Study: Computer Engineering
Areas of Expertise
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
