Barclays

I will be working at the cutting edge of AI, ML and Ops to develop our in house capabilities and create sustainable, scalable value to our customers and stakeholders.

I will do this as part of Barclays’ global CTO function.
More on this to come!

Director - Principal AI & MLOps Engineer

University of Warsaw

I give 6 hours of guest lectures to the 'AI for Executives' programme in the Faculty of Management at the University of Warsaw. The two modules I deliver cover 'AI Strategy & Project Management' and 'Fundamentals of ML and MLOps'. The detailed list of topics covered are given below:

Module 4 - AI Strategy & Project Lifecycle
1. The Foundations of a Successful AI/ML Strategy:
- What do we mean by AI?
- Where is my Data?
- Building products that are Responsible, Secure and Sustainable.
- Identifying some opportunities.
- The 4 P’s (People, Process, Pattern and Product).
- Mapping the Journey.

2. Taking Others on the AI/ML Journey
- Stakeholder Mapping.
- Building the Business Case.
- Building the Brand.

3. The AI/ML Project Lifecycle:
- The 4 Stages
-- Discover - Understanding the Problem.
-- Play - Building the POC.
-- Develop - Building the Solution.
-- Deploy - Going live and after go-live.

Module 5 - Fundamentals of ML and MLOps
1. Introduction to ‘Classic’ Machine Learning:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Natural Language Processing (NLP)

2. Machine Learning Operations (MLOps):
- What is MLOps?
- Model and Experiment Management
- ML Pipelines
- The Model Factory
- Software Development Best Practice in MLOps
- Organising for MLOps

3. Generative AI and LLMOps:
- Large Language Models
- Changing Data & Compute Requirements
- LLMOps vs MLOps
- Prompt Engineering & PromptOps
- LLM Evaluation & Monitoring
- Guardrails

4. LLMOps at Enterprise Scale
- Building an Enterprise Wide LLMOps Capability
- The Anatomy of Enterprise Scale RAG

Guest Lecturer

University of Oxford

I am a guest lecturer on the Artificial Intelligence: Generative AI, Cloud and MLOps Master's course (https://conted.ox.ac.uk/courses/artificial-intelligence-generative-ai-cloud-and-mlops-online). I gave a 90 minute lecture on "MLOps and LLMOps at Enterprise Scale". In the lecture covered the following topics and provided hands-on demo's:

1. Fundamentals of MLOps in the Enterprise:
- Is ML Appropriate?
- Why MLOps?
- Why the Cloud?
- NatWest Group Case Study

2. The ‘Ops’ of LLMOps:
- LLMOps vs MLOps
- Prompt Engineering & PromptOps
- Guardrails
- LLM Evaluation & Monitoring
- Building an Enterprise Wide LLMOps Capability (teams, operating model and COP’s)

3. GenAI (LLM) Architecture - Evolution or Revolution?
- The New Model Landscape
- The Updated Data Fabric
- Changing Compute Requirements
- Orchestration of LLM Workflows
- Deployment for GenAI
- Scalable Deployment with Ray
- Scalable RAG Deep Dive

NatWest Group

- Provide thought leadership on best practice for MLOps within the Data & Analytics function.
- Help support strategic development of technology, processes and people to make MLOps a reality across the organisation.
- Work with peers and internal stakeholders to build successful MLOps practices in their teams and to deliver ML products.
- Lead the CoE for MLOps within Data & Analytics.

Head of MLOps

- I work with colleagues across the bank to help develop, define and execute on processes and designs for engineering robust machine learning solutions. 
- Participate in several working groups supporting wider innovation and technology strategy. 
- Support and lead on technical aspects of machine learning projects, with a particular focus on taking solutions through to production.  
- Working to define an internal ML Engineering Roadmap for the data communities of practice within the technology functions of the bank.
- Help shape and drive the wider MLOps/MLEng capability across the data and analytics community of the business.

Machine Learning Engineering Lead

Aggreko

I led a team of skilled data scientists who created impactful analytics products and insight for use across Aggreko. I reported to the Director of Insight and led on the execution of the analytics pipeline of work. Responsible for driving innovation in the machine learning space for the company. All my projects supported global business objectives and utilised the latest tools, technologies and processes to ensure the team were at the forefront of industrial applications of data science and machine learning.

Analytics Team Lead

Senior data scientist in the Advanced Analytics team, helping design and develop cutting edge data science and machine learning solutions for use across the global business.

Senior Data Scientist

Streamba Ltd

Translated business problems to data problems through direct interaction with clients - ensuring we solved problems of value. Performed both internal and external stakeholder management to ensure the successful delivery of ML projects. Led R&D and deployment of data science and machine learning solutions across the business. Utilised latest techniques in machine learning, mathematical optimisation and distributed computing to deliver exceptional ROI for clients and the business.

Head of Data Science and Machine Learning

Imperial College London

Performed theoretical and computational research on hybrid perovskite photovoltaic materials and devices. I used electronic structure theory and material modelling techniques such as Density Functional Theory, Monte Carlo simulations and ab initio thermodynamics in order to understand and design new properties of these materials at the atomic level.

PhD Physics

The University of Edinburgh

Performed visiting research in the group of Prof. Neil Robertson. Continued my work on hybrid perovskite solar cells and strengthened interactions between colleagues in Imperial College London and the University of Edinburgh.

Postgraduate Visiting Research Student

October Films

Worked part-time as a scientific consultant for October Films on the Discovery Channel show’s “You Have Been Warned" and “Outrageous Acts of Science” - the shows where scientists and academics explore the weird and wonderful phenomena found in home made videos on the internet. This role consisted of me explaining the science behind the clips, writing brief summaries which were then used by on screen experts during filming.

Scientific Consultant

Hermes Summer School

Organiser

Data science, machine learning and MLOps expert with a proven track record of technical delivery across multiple industries. Experienced in model building, software development and technical management and am particularly interested in the challenge of deploying machine learning based software products at scale. I have developed solutions in logistics, energy and now in the financial services industry that have created millions of pounds of value.


- In 2024, I was named one of 20 “Rising Stars in Data and Analytics” by Driven by Data magazine and invited to give guest lectures on AI and MLOps courses at the Universities of Oxford and Warsaw.
- In 2023, the second edition of my book, “Machine Learning Engineering with Python”, was published by Packt. 
- In 2022 I was named “Rising Star of the Year” at the British Data Awards.
- In 2019 I was named “Data Scientist of the Year” by the Data Science Foundation.

Hey, Data Analysts!

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➡️ Data Scientist - Michael Green, 🔥 Matt Dancho 🔥, Andrew Jones
➡️ Data Engineer - Benjamin Rogojan, Joe Reis 🤓, Andreas Welsch
➡️ Analytics Engineer - Madison Schott, Mark Chappell, Christian
➡️ ML Engineer - Chip Huyen, Andriy Burkov, Andy McMahon
➡️ Decision Scientist - Nisar Ahmed, Cassie Kozyrkov, Joe Dery

Which position interests you the most?

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