I am currently debating whether I’m committed to finishing my PhD or not. As a result I might be open to suitable opportunities for either part-time or contract work in the areas listed below. For each area I’ve listed a few sub-areas that I have experience with or I am interested in doing further work in.

Please contact me at mail:// for further information.

DevOps Engineering

  • Developing and maintaining CI/CD pipelines for GitHub Actions / GitLab Runners / Jenkins instances, including automated testing and infrastructure provisioning
  • Helping teams to identify and transition to suitable architecture and infrastructure for new/existing products (e.g. transitioning to a services based architecture using Docker Swarm and/or Kubernetes for container orchestration)
  • Deploying and maintaining backend support infrastructure such as: ELK stacks for logging and/or automated reporting/metrics/alerts; authentication and user management systems; custom APIs for simple access to common infrastructure and backend services
  • Currently have intimate experience with on-premises GPU servers for Deep Learning (currently maintain several GPU enabled dockerspawner JupyterHub servers) and have experience with test deployments of and Kubeflow products

Data Engineering

  • Building and maintaining backend data systems/services to facilitate data teams in finding insights/building data products more quickly, including building out feature stores for machine learning projects
  • Creating and optimising reliable and performant ETL pipelines for “big data” ingestion (I have a particularly special place in my heart for time series audio data)
  • MLOps (a.k.a. DevOps for machine learning) work including: CI/CD pipelines for model training, testing and evaluation phases; automatic horizontal scaling of infrastructure based on training/inference workloads
  • Writing and optimising APIs for machine learning code that are performant and reliable, whilst retaining flexibility for future expansion if required

Linux System Administration

If your linux servers are [exploding]/[on fire]/[responding slowly] then I will probably be able to extinguish the fires. I’ll tell you up front if I don’t think I can fix it.


Below is a non-exhaustive list of different technologies I have experience with (these are just the ones that ended up in production!). I do not proclaim to be an expert with many of these technologies as I’m a typical “jack of all trades, master of none“. The list is loosely ranked in descending order of experience.

Docker (incl. Docker Swarm), Python, JupyterHub (incl. Administration), Amazon Web Services (we used quite a lot of AWS services in a previous team), Digital Ocean, Tensorflow (extensive experience primarily with the V1 API), various database systems (MongoDB; Redis; Postres; MySQL), Kubernetes, Ansible, GitHub Actions, GitLab Runners, Jenkins (including groovy pipelines), Apache Spark, Apache Airflow, systemd units…

I’m also pretty handy with Microsoft Excel (I prefer anĀ  INDEX(MATCH()) over a VLOOKUP).