Alexius Wronka
CTO of Data and Growth at Invisible Technologies
New York City Metropolitan Area, United States
About Alexius Wronka
Alexius Wronka is CTO of Data and Growth at Invisible Technologies. Location: New York City Metropolitan Area, United States.
I am a technology and data leader focused on one thing: making AI actually work in the enterprise. Today, I serve as CTO of Data at Invisible Technologies, where I build and deploy AI-powered data platforms that move organizations from fragmented systems and manual workflows to production-grade, ROI-generating AI. My work sits at the intersection of data engineering, machine learning, and real-world operations. I focus on turning messy, incomplete, and unstructured data into usable systems that drive decisions across finance, retail, healthcare, government, and private equity. A core part of my work is building the data and context foundation required for modern AI systems. This includes generating synthetic data to augment sparse or biased datasets, designing large-scale labeling and annotation pipelines, and creating high-quality training datasets for domain-specific models. I have developed and deployed computer vision systems in real-world environments and built end-to-end pipelines for training, fine-tuning, and evaluating models. I lead work across prompt engineering, context engineering, and model fine-tuning, including reinforcement learning workflows and evaluation systems that improve reliability in production. My focus is not just model performance, but ensuring systems are grounded in the right data, context, and feedback loops to drive real outcomes. Prior to Invisible, I was a Partner at McKinsey & Company (QuantumBlack), where I led large-scale data and AI transformations for Fortune 500 companies and private equity firms. I built end-to-end data platforms, designed decision systems, and led cross-functional teams across engineering, analytics, and operations. I started my career as a software engineer, giving me a hands-on foundation in building systems, not just advising on them. I am particularly interested in: Enterprise AI infrastructure, not just models Agentic systems and data platforms Synthetic data generation and AI training pipelines Private equity data value creation Computer vision and real-world sensing systems I believe the gap in AI is not model capability. It is data, context, and execution.
Skills
Additional experience
Partner
McKinsey & Company
December 2023 – March 2025
Education
- Bachelor’s Degree, Syracuse University