Curated Overview

My CV

Staff-Level Snapshot

A fast-read view of what I build, how I think, and where I create leverage: streaming systems, ML platforms, production engineering, and standards-facing AI work.

🧠 Positioning

Staff-level machine learning engineer building real-time, production-grade AI systems

I design and operate ML systems that sit on live data, serve user-facing decisions, and have to remain reliable under real-world constraints. My work spans streaming infrastructure, ML systems, backend engineering, and the operational discipline needed to make production the source of truth.

  • ⚙️ Kafka + Flink
  • 🧠 Inference systems
  • 🛰️ Geospatial & temporal data
  • 📈 Forecasting pipelines
  • 👥 Team leadership
  • 🏛️ Standards work
Download PDF CV Open Experience Timeline

🧭 What I Actually Do

  • Build end-to-end ML systems From raw signal ingestion to user-facing predictions and analytics.
  • Design streaming architectures Low-latency inference, feedback loops, replayability, and failure handling.
  • Bridge ML and real-world constraints Aligning model output with business logic, operational rules, and product decisions.
  • Lead teams toward measurable outcomes Hiring, mentoring, planning, technical direction, and production readiness.

🚢 Selected Work

  • Real-time vessel destination and ETA prediction systems operating on global AIS streams.
  • Streaming pipelines spanning ingestion → normalisation → ML inference → analytics delivery.
  • AIS denoising and signal-quality improvement using stateful filtering approaches.
  • Event-driven ML architectures with backfills, observability, and production rollout discipline.
  • Internal platform work that reduced technical debt and standardised ML delivery patterns across repositories.

📐 Core Principles

  • Production is the only truth.
  • If it cannot be measured, it is not done.
  • Deterministic systems beat clever hacks.
  • Models must degrade gracefully.
  • System quality should come through people and process, not heroics.

📚 Career Snapshot

  • Since 12/2020 · Vortexa, London
    Staff-level ML tech lead and pod lead for streaming-first maritime forecasting systems.
  • 2016–2020 · Data Reply, London
    Progressed from data science into ML engineering, delivering systems across Vodafone, CNHi, and UBS.
  • 2010–2016 · KCL, UCL, GSM, David Game College
    Teaching and academic roles across computing, AI, software, and data subjects.

🏛️ Standards & Research

  • Since 10/2024 · UCL
    Associate Researcher supporting AI application, standardisation, and ethics initiatives.
  • Since 01/2021 · ISO JTC 21 WG3
    Committee Expert Member contributing to AI standards aligned with EU policy and international norms.

🎙️ Talks & Interviews

  • 2023 Agile in Action podcast interview on the Vortexa journey and agile data science.
  • 2022 ODSC talk on dynamicio and abstracting I/O in ML systems.
  • 2020 iunera interview blog on the agile approach in data science.
  • 2020 Big Data Warsaw talk on monitoring communication and trade events as graphs.
  • 2018 Connected Data London panel and Minds Mastering Machines talk.

🎓 Education & Credentials

  • Ph.D. in Computer Science · King’s College London
    Persuasion dialogues, opponent modelling, knowledge graphs, Bayesian techniques, and formal semantics.
  • Diploma (BEng) in Computer Engineering · University of Thessaly
    Polytechnic training with a strong focus on mathematics and artificial intelligence.
  • Selected certifications
    AWS ML Specialty, Google Data Engineer, Process Mining, Graph Analytics for Big Data, Neo4j, and Elasticsearch.