Career Profile
AI Engineer with over 4 years of experience spanning AI quality assurance, ML model development, and AI consulting. Proven ability to design, evaluate, and deploy machine learning solutions, ensuring fairness, explainability, and robustness. Combines technical expertise with client-facing skills from consulting roles, delivering scalable AI systems that solve complex business challenges. Passionate about AI development and innovation.
Professional Experience
I primarily work on iQ4AI, an enterprise AI testing and auditing platform, developing core evaluation components and backend services. In parallel, I build LLM-powered tools (RAG and agentic workflows) for internal and client use, supporting automated analysis, reporting, and client-facing AI solutions. Responsibilities
- Designing, implementing and maintained core AI services focused on XAI (SHAP/LIME), fairness, robustness and performance monitoring, ensuring high reliability for production testing.
- Developing LLM-assisted modules for automated analysis and reporting of AI evaluation results, reducing manual review time and improving consistency across evaluation outputs.
- Collaborating closely with product, engineering, and consulting teams to deliver AI features from technical design to client demos.
- Converting research prototypes into production ready features, improving execution efficiency and computational performance.
- Accelerated platform readiness for MVP and alpha releases by designing and implementing core AI evaluation features.
- Contributed to the productionization of a RAG-based Q&A system for insurance-sector clients, transitioning from research prototypes to scalable microservices used in client-facing or internal decision workflows.
- Delivered live technical demos and presentations to 10+ enterprise clients, contributing to a 80% customer retention rate.
In this role, I worked directly with enterprise clients on the assessment and evaluation of AI systems, particularly in regulated and high-impact contexts. Responsibilities
- Evaluated the performance, fairness, security and explainability of AI systems, working closely with engineering teams, providing actionable insights for AI risk assessment and mitigation.
- Conducted AI system audits for 10+ enterprise AI models, ensuring compliance with ISO standards and AI regulations.
- Translated complex business and regulatory needs into actionable ML requirements and system specifications.
- Contributed to AI due diligence efforts for 2 major acquisitions, evaluating technical feasibility, scalability, and potential risks in AI models and data infrastructure, contributing to successful investment decisions.
- Conducted comprehensive analysis of 3 AI systems for a leading bank, identifying critical issues related to model performance, bias/fairness, and compliance with regulatory standards.
- Developed an AI risk assessment tool, reducing AI compliance validation time by 60%.
Responsibilities
- Performed data annotation, preparation and engineering for training AI models.
- Applied Machine learning unsupervised (Self-Organizing Maps, Gaussian Mixture Models) and supervised learning (Random Forest, SVM, XGBoost, AdaBoost, Logistic Regression, KNN, CART) algorithms to real-world AI problems.
- Facilitated discussions with project partners and other stakeholders.
- Involved in 2 EU-Funded (Horizon 2020 research and innovation program) projects (Kyklos 4.0 and Stargate) developing AI solutions for sustainability and manufacturing.
- Prepared part of a technical deliverable for Task 3.5 of Kyklos 4.0 Project, influencing EU-funded AI research initiatives.
- Built a deep learning model (U-Net) in Keras Tensorflow for a semantic image segmentation task (weed detection) achieving 92% accuracy on test data, reducing manual weed removal efforts by 40% through automated identification.