I’m Christos. I lead teams that turn research and complex data into reliable products.
Engineering Manager for production ML and data platforms, combining people development, technical direction and delivery.
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 for production ML and data platforms. At Vortexa, I manage six direct reports and lead a ten-person cross-functional team responsible for live ML and data services supporting maritime intelligence. I joined as a Senior ML Engineer in December 2020 and became Engineering Manager in February 2022. My responsibility combines people development, technical direction and delivery.
I build teams that can own complex systems together. That has meant developing colleagues through feedback and mentoring, shaping hiring and onboarding as Data Production grew from four to more than thirty people, and making changes easier to test and review. Local end-to-end testing cut a three-hour pipeline development feedback loop to under five minutes. I also led the Kafka Streams-to-Flink migration and started a weekly architecture forum to share decisions and reduce knowledge silos.
My industry career began in 2016 with data science and ML engineering consulting, following doctoral research and teaching. Today I stay close to architecture and production while helping engineers, Product and domain experts agree priorities and measures of quality. Alongside this work, I am a UCL Associate Researcher and an AI standards committee expert, researching and teaching practical AI standardisation, auditability and responsible adoption.
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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