Curated Overview

My CV

Engineering Manager Snapshot

A fast-read view of the same story as the PDF: engineering management, production ML, live streaming systems, delivery discipline, and practical AI standards.

🧠 Positioning

Engineering Manager for production ML and real-time data systems

I build engineering organisations, operating models, and platforms that keep ML systems dependable under real production pressure. At Vortexa, that means owning engineering strategy and delivery for a major live streaming backend estate, leading a 10-person cross-functional team around it, and keeping reliability, evaluation, explainability, and delivery health close to the work.

  • 👥 Team leadership
  • ⚙️ Live systems
  • 🧠 ML operations
  • 🛡️ Client-facing reliability
  • 📈 Delivery health
  • 🏛️ AI standards
Download PDF CV Open Experience Timeline
Christos Hadjinikolis presenting at Big Data London in 2018

🧭 What I Actually Do

  • Build and manage technical teams Hiring, mentoring, reviews, progression, onboarding, delivery governance, and cross-functional operating rhythm.
  • Own live-system strategy Client-facing streaming estate, platform evolution, replayability, monitoring, and failure handling.
  • Turn ML ambiguity into operating discipline Evaluation loops, model/data versioning, analyst feedback, product semantics, and prediction trust.
  • Make delivery repeatable Standardisation, docs close to code, tests-as-docs, ADRs, local E2E tests, DORA/Jira signals, and healthier feedback loops.

📌 Evidence Behind The CV

  • Organisation: drove Data Production Team growth from 4 to 30+ people by shaping hiring loops, system-design evaluations, mentoring, and delivery standards.
  • Estate: own a major client-facing live streaming backend area filtering roughly 6M records/hour into production intelligence.
  • People: lead a 10-person cross-functional team around the estate, with direct management of the MLE/DE/DS core and close Product/SME partnership.
  • Reliability: protect production trust through monitoring, runbooks, fallback paths, Jira alert workflows, and MTTR kept under 30 minutes.
  • Practice: scale delivery through Flink strategy, ADRs, local E2E/integration tests, docs close to code, DORA/Jira signals, and reduced single-owner bottlenecks.

📐 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 clear ownership, evidence, and repeatable practice.

📚 Career Snapshot

  • 12/2020–present · Vortexa, London
    Engineering Manager / ML Systems Lead owning engineering strategy and delivery for Vortexa's live streaming intelligence estate, driving Data Production Team growth from 4 to 30+, and leading a 10-person cross-functional team around the estate.
  • 04/2016–12/2020 · Data Reply, London
    Senior Consultant and first London spin-off consultant; grew from data scientist into ML engineer while supporting team growth, client delivery, mentoring, and project leadership 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 helping students connect AI standards, the AI Act, auditability, explainability, and practical AI adoption.
  • Since 01/2021 · ISO/CEN-CENELEC JTC 21 WG3
    Committee Expert Member advocating practical AI standards for auditability, model/data versioning, explainability, and safer adoption.

🎙️ Talks & Interviews

  • 2023 Agile in Action podcast interview on the Vortexa journey and agile data science.
  • 2022 ODSC talk on dynamicio, a published PyPI library for 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.