Narrative Timeline
Professional Experience
How the scope evolved
Not a second CV. This page shows how the scope widened over time: from teaching and research, into consulting under constraints, and then into engineering management for production ML and live data systems.
🧭 Professional Arc
From research and teaching to Engineering Management for real-time ML systems
This page is intentionally not a second CV. It is the story of how the same pattern kept widening: make complex work understandable, turn ambiguity into structure, and build the teams, interfaces, and operating models needed to make ML useful in production.
- 🎓 Research roots
- 🏗️ Production ML
- ⚙️ Streaming architecture
- 👥 Engineering management
- 📈 Delivery health
- 🏛️ Standards work
✨ What Changed Along The Way
- Early years: explaining and teaching I learned to break complex ideas down clearly and help others build confidence in technical subjects.
- Consulting years: delivery under ambiguity I learned how messy systems, unclear requirements, product pressure, and client constraints reshape "correct" engineering.
- Current years: management and leverage I now focus on team growth, architecture, operational quality, stakeholder alignment, and repeatable delivery systems.
2010–2016
Foundations
teaching, research, communication
teaching, research, communication
🎓 Teaching, doctoral work, and the habit of clarity
KCL · UCL · GSM · David Game College
Associate Lecturer · Coding Teacher · TA
Before I was responsible for production systems, I spent years teaching and researching, which is where I developed the habit of explaining difficult ideas simply and structuring technical work carefully.
What I was doing
- Teaching Java, Python, MATLAB, HTML, CSS, SQL, AI, systems, and data structures.
- Completing doctoral research in persuasion dialogues, opponent modelling, and large knowledge graphs.
- Working close to formal methods, graph reasoning, and research-driven problem solving.
What stayed with me
- Technical communication is a force multiplier.
- Good systems thinking starts with clean abstractions.
- Explaining something clearly is often the best test of understanding it.
04/2016–12/2020
Consulting
shipping under constraints
shipping under constraints
🏗️ Consulting became the bridge from data science to technical leadership
Data Reply
London, UK
Data Scientist → ML Engineer → Senior Consultant
This was the period where model work became inseparable from delivery discipline. I joined the London spin-off as its first consultant, grew into a Senior Consultant, helped the team scale, and learned to turn enterprise constraints, client goals, and production expectations into deliverable ML/data systems.
Representative client work
- Client delivery: led client meetings, scoped goals, facilitated technical delivery, placed consultants, interviewed, mentored, and contributed to Data Reply's growth from a small founding team to 30+ consultants.
- 🏦 UBS: graph analytics, process mining, and real-time insight pipelines with Kafka, Elasticsearch, and Python; learned XP/pairing practices and later became the sole embedded Data Reply consultant.
- 🚜 CNHi: lead data scientist / Scrum Master for a DS team processing live vehicle sensory data with PySpark to infer maintenance needs.
- 📱 Vodafone: led technical delivery across multiple workstreams, worked on Infinity, a GCP/Kubeflow data-science platform, and built Red Agent, a mobile-network feature-engineering framework.
What this phase taught me
- Most ML failures are systems failures, not modelling failures.
- Ambiguous environments are where architecture and product discipline matter most.
- Bridging DS, engineering, and product is a delivery problem as much as a technical one.
- Good managers create feedback loops that make specialists faster, safer, and less dependent on individual memory.
12/2020–present
Management
teams, systems, reliability
teams, systems, reliability
🚢 Vortexa: managing teams and live ML/data systems at scale
Engineering Manager / ML Systems Lead
London, UK
Architecture · Delivery · People
At Vortexa, the centre of gravity shifted again: from delivering components to owning engineering strategy and delivery for a major client-facing live streaming backend estate, setting operating standards, and leading a 10-person cross-functional team around it.
What I lead
- A 10-person cross-functional team: 6 MLE/DE/DS, 2 Product, and 2 SMEs.
- Direct management of the MLE/DE/DS core, with indirect leadership across product and domain partners.
- 1:1s, reviews, promotion input, hiring, retention, onboarding, sprint reviews, retrospectives, mentoring, and code pairing.
- Hiring and interview loops: developed hiring practices and system design evaluations; hiring manager for 6 roles, 31+ candidates interviewed in that capacity, and 60+ interview loops overall across Data Production staffing; retained the current team fully, with every member recently promoted.
- Team practices that reduce single-person ownership: clearer ownership, pairing, docs close to code, tests-as-docs, and onboarding that makes new joiners productive in production code within their first week.
What the estate requires
- A client-facing live streaming intelligence estate processing roughly 6M filtered vessel-position records/hour, focused on 13.5K monitored vessels.
- Two-year Kafka Streams-to-Flink transformation strategy, establishing Flink as a future-proofed platform direction aligned with company growth and using its dataflow model, independent state/checkpointing, and operational UI to reduce cognitive load and partition-coupled scaling.
- Monitoring, runbooks, rollback/fallback paths, and Jira alert workflows keeping MTTR under 30 minutes.
- DORA/Jira delivery signals, architecture forums, ADRs, dev containers, Backstage adoption, and local E2E/integration tests to make delivery visible, healthier, faster, and safer; a recent 12-month view showed 14.65 deploys/week and 0.52% change failure.
🏛️ Beyond The Core Role
Standards and research
- Since 01/2021 · ISO/CEN-CENELEC JTC 21 WG3
Committee Expert Member working on AI standards aligned with international and EU policy directions. - Since 10/2024 · UCL Department of Information Studies
Associate Researcher helping expose students to practical AI applications and lecturing on AI standardisation, the AI Act, auditability, versioning, explainability, and safe adoption.
🎙️ Public Work
Talks and interviews
- 2023 · Agile in Action
Podcast interview on agile data science and the Vortexa journey. - 2022 · ODSC
Industry talk on dynamicio, a published PyPI library for abstracting I/O in ML systems. - 2020 · iunera & Big Data Warsaw
Interview and conference talk on agile data science and graph-driven analytics. - 2018 · Connected Data London & Minds Mastering Machines
Panel and talk appearances on graph AI and doing data science the agile way.
📐 Through-Line
What has remained constant
- Production is the only truth.
- Standards set teams free when they remove avoidable ambiguity.
- Models need auditability, monitoring, and graceful failure paths.
- Healthy delivery needs visible signals, not hidden stress.
- System quality should scale through clear ownership, evidence, and repeatable practice.