Career Profile
AI Engineer with over 3 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
Responsibilities
- Core contributor to the design and development of the company’s flagship AI testing and auditing platform, collaborating with AI engineers, data scientists, and software engineers.
- Led feasibility analysis and implemented 5+ critical AI features, including model performance evaluation, fairness/bias testing, explainability analysis, statistical analysis and drift detection.
- Converted 2+ successful 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.
- Delivered live technical demos and presentations to 10+ enterprise clients, contributing to a 80% customer retention rate.
- Automated 60% of the platform’s core evaluation workflows, cutting manual assessment efforts in half and improving efficiency.
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.
- Provided technical guidance on AI model tuning, data preprocessing, and optimization to cross- functional teams, ensuring successful delivery within scope, timeline, and budget constraints.
- 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 50%.
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.