Curated Overview

My CV

Engineering Manager Snapshot

Engineering Manager for production ML and data platforms: people development, technical direction and delivery, with explicit career progression and supported outcomes.

🧠 Positioning

Engineering Manager for production ML and data platforms

I manage six direct reports and lead a ten-person cross-functional team at Vortexa, with responsibility for people development, technical direction and delivery. I became Engineering Manager in February 2022 after joining as a Senior ML Engineer in December 2020. My teams turn research and complex data into reliable products.

  • 👥 Team leadership
  • 🧠 Applied ML systems
  • 🌱 People development
  • 🧭 Technical direction
  • 🛡️ Client-facing reliability
  • 🏛️ AI standards
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Christos Hadjinikolis presenting at Big Data London in 2018

🧭 What I Actually Do

  • Build and manage technical teams Manage six direct reports, coach colleagues, run performance and career reviews, and set delivery priorities with a ten-person cross-functional team.
  • Move ML work from research towards production Led delivery of destination and arrival-time models, with versioning, deployment, evaluation and monitoring so the team can improve production predictions.
  • Agree priorities across disciplines Bring engineers, Product and domain experts together to resolve competing definitions of model quality and turn them into measurable objectives and prioritised work.
  • Help teams deliver with confidence Make changes easier to test and review, share architecture decisions, and reduce dependence on individual experts.

📌 Evidence Behind The CV

  • People: manage six direct reports and lead a ten-person cross-functional team. Retained the full team and supported every member’s promotion or progression in the July 2026 team snapshot.
  • Hiring and growth: hiring manager for six roles over time; shaped hiring, system-design interviews, onboarding and mentoring as Data Production grew from four to more than thirty people.
  • Delivery: cut a three-hour pipeline development feedback loop to under five minutes through local end-to-end tests and reusable data-access patterns.
  • Estate: own engineering strategy and delivery for a live ML/data estate turning roughly 6M vessel-position records/hour into production intelligence for 13.5K monitored vessels.
  • ML delivery: led 0-to-1 research-to-production delivery for destination and arrival-time sequence/transformer models in PyTorch.
  • Evaluation: established batch/online model-evaluation and replay loops, analysed failure modes with domain experts and Product, and converted findings into model, data, and interface improvements.
  • Platform direction: led the Kafka Streams-to-Flink migration, enabling compute scaling independent of Kafka partitioning and improving operational visibility and maintainability; established shared on-call, runbooks and rollback/fallback practices.

🧪 Applied AI & Tooling

  • Promet: personal project exploring local AI assistant runtimes, voice, tool execution, approval boundaries, durable state and trace/replay workflows.
  • skeleton-replay: public Python tooling that turns script/pytest runs into traces, architecture snapshots, workflow evidence, and replayable reports for review, debugging, onboarding, and LLM-assisted code understanding.
  • Skeleton Replay plugin: PyCharm/IntelliJ workflow that brings runtime evidence and source navigation into the IDE.
  • dynamicio: Python library for explicit data-access boundaries and schema validation, used across 15+ repositories to support local testing and reusable ML/data workflows.

📚 Career Snapshot

  • 02/2022–present · Vortexa, London
    Engineering Manager / ML Systems Lead: six direct reports, a ten-person cross-functional team, and responsibility for people development, technical direction and delivery.
  • 12/2020–02/2022 · Vortexa, London
    Senior ML Engineer, before progressing into engineering management.
  • 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 2021 · ISO/CEN-CENELEC JTC 21 WG3
    Committee Expert Member contributing to AI standards aligned with EU policy and international norms, with emphasis on auditability, model/data versioning, explainability, and safer adoption.

📐 Core Principles

  • Production is the only truth.
  • Models need evaluation, replay, monitoring, and graceful failure paths.
  • Responsible AI is partly an engineering discipline: evidence, auditability, ownership, and human accountability.
  • System quality should come through clear ownership, measurable interfaces, and repeatable practice.

🎙️ 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 historical credentials
    AWS Machine Learning Specialty (2020), Google Professional Data Engineer (2017), Apache Spark Developer (2016).