Gleb Lukicov MLOps Leader | PhD in Physics

Gleb Lukicov

Empowering data science teams to succeed

gleb@pipeline: ~/about zsh

$gleb --about

Hey there, I'm Gleb, and I'm passionate about empowering data science teams to succeed by building reliable and scalable MLOps solutions — because effective MLOps isn't just about tools; it's about aligning people, processes, and platforms to solve real business problems.

Outside of work, I co-organise the Agentic AI Foundation (AAIF) Community London — Europe's largest AI engineering meetup — and love sharing what I've learned through blogging and public speaking.

Before entering the tech industry, I conducted scientific research at world-class physics labs in the US and Switzerland, working on large-scale, multidisciplinary projects. In my spare time, I enjoy road cycling and running.

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stage 01

Agentic AI Foundation (AAIF) Community London

running
inputs
agentic AI trends · pizza
outputs
regular in-person meet-ups in London
role
co-host and events organiser · largest AI engineering community in Europe
stage 02

ML projects and articles

running
inputs
repositories · ML pipelines · GPUs · Docker, uv, Google Cloud
outputs
2 open-source projects · 4 articles · 3 videos · 1 podcast
awards
Google Cloud DevOps award, 2022 and 2023
repo

SlideOps: decks that tell you when they go stale

SlideOps: turn a repo into a deck that tells you when it stops matching the code, shown beside the demo deck's title slide

Turn a repository into a slide deck that tells you when it stops matching the code. SlideOps is a pair of Agent Skills: one builds a single-file HTML deck from a repo, the other checks that deck later. Every reference in the deck carries the file it came from, the exact line range, and a hash of those source lines, so the check is standard-library Python: no model, no network, zero tokens, milliseconds.

The repo is its own plugin marketplace, so in Claude Code it is two lines: add the marketplace,

/plugin marketplace add glukicov/slideops

then install the plugin from it:

/plugin install slideops@slideops

Any agent with the skills CLI gets it in one line:

npx skills add glukicov/slideops

It also runs on Codex, Copilot CLI and OpenCode: all three read ~/.agents/skills, and ./install.sh in a repo puts the skills where each of them looks.

article

To MLOps, or not to MLOps?

MLOps Article

That is the question — the platform is the answer. How does a correct MLOps strategy enable success for data science projects in a large organisation? What is an effective MLOps team?

article

ML pipelines in the age of LLMs

ML Pipelines

Are you an AI engineer trying to bridge local development and production deployment of ML projects? Find out how pipelines enable scalable experimentation with Docker and uv on Google Cloud.

video

Virgin Media O2: DevOps journey

In this video, I share my professional journey at Virgin Media O2, where we are utilising the power of Google Cloud to enable us to make data-driven decisions that improve our customers' experiences. For this work, our team won the DevOps award in 2022 and 2023.

video

Interview with the Royal Statistical Society

In this interview, I share my journey from particle physics to data science and MLOps, and how I discovered a passion for building data platforms and helping teams work with data at scale. I talk about my path into the field, my proudest moments as an ML engineering manager, and the importance of good fundamentals, community, and clean data.

podcast

Electric Twin & MLOps Community London

In this podcast, I discuss why your business might benefit from MLOps, MLOps Community London meetups and the exciting work we do at Electric Twin on simulating human behaviour in real-time with AI.

article

Personal GPU server for ML

GPU Server

With Ubuntu, CUDA and port-forwarding. I cover how to set up secure remote access over ssh, wake-on-LAN feature to switch on your GPU server remotely and mounting the remote file system on your laptop.

post

Electric Twin is ISO 27001 certified

Electric Twin ISO 27001 certification

Startups move fast, and as it turns out, you can move fast on security & compliance too!

video

Inspired to Build: Live with Gleb Lukicov

Live conversation with Erica Hughberg about scaling MLOps at Electric Twin and what inspired my journey into data & AI.

reading

Some great resources that helped me on my data science and ML journey

Technical

Career

stage 03

Research overview

succeeded
inputs
200 collaborators · 20 GB/s of raw detector data · 28 NVIDIA Tesla K40 GPUs
outputs
PhD thesis · arXiv:1909.12900 · alignTrack · EDMTracking · APS DPF talk
metrics
+3% data yield · +4% data quality · 200 MB/s recorded
duration
DAQ on-call, 2017–2019
fig. muons circulating in the g − 2 storage ring, seen from above. Each carries a spin arrow that precesses ahead of its direction of travel — the anomaly the experiment measures. A muon decays; the positron curls inward to one of the 24 calorimeter stations.
repo

Software development

To accurately measure this anomaly, a calibration of the tracking detectors is required. The main project of my PhD involved developing, testing and deploying the calibration software framework, which increased the yield of data by 3% and the data quality by 4%. The alignment procedure is a statistical problem, which involved the optimisation of the p-values of tracks via matrix inversion.

repo

Data analysis

There is also an additional measurement that will be made using the tracking detectors: setting a new limit on the electric dipole moment (EDM) of the muon, producing a world-leading result. I have been developing algorithms to measure the muon EDM, by analysing large and complex datasets using regression and Fourier transform methods.

talk

American Physical Society, Boston

Presenting at the American Physical Society conference

Presenting at the American Physical Society conference in Boston. My talk highlighted the significance of precise tracking systems in high-energy physics experiments and their role in probing fundamental particle properties.

ops

Data collection

An essential component of the experiment is the data acquisition (DAQ) system, which manages the data flow from the detector electronics. The experiment is acquiring raw data at a rate of 20 GB/s. This is accomplished by employing a parallel data processing architecture using 28 high-speed GPUs (NVIDIA Tesla K40), reducing the recorded data rate to 200 MB/s. To support the smooth operation of the experiment and ensure continuous data taking, a team of data acquisition (DAQ) experts are available for 24/7 support. I have actively participated as the DAQ on-call computing expert between 2017 and 2019.

stage 04

Education projects

running
inputs
muons · spectroscopy · Raspberry Pi
outputs
alumni talks · public tours of Muon g − 2 · 1 article
reach
300 pupils a year · Your Universe, 2010–2015 · Woodhouse College, annually
photo

Public tour of the Muon g − 2 experiment

Fermilab Tour

Guiding a public tour of the Fermilab Muon g — 2 experiment. The experiment has measured an anomaly in a property of a fundamental particle, expanding our knowledge of the Universe and providing evidence of the existence of new particles.

article

A personal Bitcoin access point with Raspberry Pi

Medium Article

I wrote an article about building a personal Bitcoin access point with Raspberry Pi. Read to learn about using an Electrum node for interacting with the decentralised blockchain network.

photo

Your Universe festival, UCL

Your Universe Festival

Explaining spectroscopy to primary school students at the Your Universe astronomy festival at UCL. I was involved with the festival between 2010 and 2015. Every year, I was able to deliver educational activities to 300 pupils. I also had a chance to meet Geraldine Cox during the festival.

stage 05

Personal interests

succeeded
inputs
1 marathon · 435 km cycled · 4.9 km of elevation
outputs
£6.1K raised for Street Child
duration
two years, back to back
photo

First marathon and a 100 km ride for Street Child

Charity Marathon

As part of a charity fundraising, I ran my first full marathon (5:31 / 1 km elevation) and cycled 100 km (4:44 / 2 km elevation). This motivated me all the way (twice!) to the top of that mountain, and our team manged to fundraise £2.3K for Street Child.

photo

A mini tour of the Netherlands for Street Child

Charity Cycling

Next year, our team was back at it! With a mini tour-of-Netherlands: 160 km day 1 (6:28 / 0.8 km elevation) and 175 km day 2 (6:37 / 1.1 km elevation). This time, we have raised £3.8K for Street Child.