I’m Christos. I lead teams that build real-time, production-grade intelligence systems.
Engineering Manager and ML systems lead working across live data processing, streaming infrastructure, production ML, and the operating practices that make complex systems trustworthy.
Portfolio
This is a selection of public work that reflects how I think about ML systems in practice: production reliability, developer feedback loops, schema discipline, graph-driven reasoning, and the translation of research ideas into maintainable software.
Some of the most important systems I lead are private and production-facing, but the projects below show the engineering themes that run through my work: turning ambiguity into tooling, making interfaces explicit, helping teams reason about runtime behaviour, and shipping ML/data systems that remain understandable after the demo.
The developer tools here are not just repositories: dynamicio and skeleton-replay are published PyPI libraries, and Skeleton Replay also ships as a JetBrains Marketplace plugin. That matters because packaging, documentation, release discipline, and user workflow design are part of the work.
Skeleton / skeleton-replay
A published developer-tooling stack for replaying Python runtime architecture, turning traces into evidence-backed reports for humans and LLM-assisted review. PyPI · JetBrains Marketplace · GitHub
Harmonizing Avro and Python
A production-minded take on schema management, code generation, and keeping data contracts aligned across systems.
Mining Public Opinion on Twitter
An applied NLP project focused on extracting signal from noisy public data rather than idealised benchmark conditions.
dynamicio
A published PyPI library for ML/data workflows that makes I/O seams explicit, supports schema/data validation, and shortens feedback loops by testing business logic against characteristic local samples. PyPI · ODSC talk
Company Neighbourhood Instantiator
A graph-oriented system for reasoning about company relationships and neighbourhood structure in complex data landscapes.
My PhD Thesis: Persuasion Dialogues & Opponent Modelling
The research foundation behind my long-term interest in reasoning systems, graph structure, and formal approaches to AI.
About Me
I am an Engineering Manager and technical ML systems leader focused on real-time, production-grade intelligence systems. At Vortexa, I own and evolve the live streaming intelligence estate, a major client-facing part of the company's backend platform where correctness, reliability, and prediction trust matter every day. Over almost six years, I drove Vortexa's Data Production Team from 4 people to 30+ and raised its operating maturity by shaping hiring loops, system design culture, strategic platform choices, and a cross-functional operating model around engineers, Product, analysts, and SMEs.
Over 16 years across research, teaching, consulting, and production ML, the scope of my work has moved from explaining and building models to leading the people, interfaces, standards, and reliability practices that make models useful. I care about the hard middle: data contracts, observability, rollback paths, evaluation loops, prediction trust, and team habits that reduce cognitive load and spread ownership.
My management style is team-first and evidence-led: hire well, mentor deliberately, pair when it helps, keep decisions close to code, use DORA/Jira signals to expose delivery friction, and cultivate trust through clear feedback. Alongside Vortexa, I am a UCL Information Studies Research Associate and an ISO/CEN-CENELEC JTC'21 committee expert. I research and teach practical AI standardisation, including the AI Act, auditability, model/data versioning, explainability, and why good standards make responsible adoption easier rather than slower.
Welcome to ML-Affairs!
Welcome to ML-Affairs, a space where I, Christos, share my journey and insights in the world of machine learning and AI. This site is a reflection of my passion for applied data science and a place for those who seek a nuanced perspective in this dynamic field. As a seasoned ML Engineer and a continuous learner, I aim to explore and discuss the latest trends, challenges, and breakthroughs in AI. Whether you are a fellow enthusiast or a professional in the field, I hope to offer a refreshing and informative experience.
More specifically, this site is where I write about:
- streaming-first ML systems
- the gap between experimentation and production
- event-driven architectures for inference and decision systems
- the trade-offs behind building reliable AI under messy real-world constraints
If you are interested in ML systems that have to run continuously, degrade gracefully, and support real decisions, you will probably feel at home here.
Join me in this exploration by connecting with me on LinkedIn.
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