09.09.2026
Senior MLOps Engineer
ML, AWS, Python
Join a senior engineering role owning the environment in which machine learning models are built — the data pipelines and data lake integrations that feed model training, the tooling and workflows data scientists rely on, the quality and validation checks a model has to pass, and the flow that takes a validated model through to release.
Start
21.09.2026, ASAP
Duration
3months +
Location
Remote
Allocation
100%
The company builds AI-driven cybersecurity, device intelligence, network intelligence, and parental control solutions for network service providers, protecting millions of homes, and operates in a regulated, governed environment.
This is a model-and-data role rather than a production operations role — you'll work closely with data scientists in a focused, experimentation-friendly environment, then hand off to the engineers who run the customer-facing services (a separate team).
This is a model-and-data role rather than a production operations role — you'll work closely with data scientists in a focused, experimentation-friendly environment, then hand off to the engineers who run the customer-facing services (a separate team).
In this role, you will:
- Own the model building and training process — the environment, tooling, and workflows that take data scientists from raw data to a validated, release-ready model.
- Build and maintain data lake integrations and the data pipelines that feed model training.
- Own model quality and validation — reproducible training runs, evaluation, and the checks a model must pass before release.
- Run model release and publishing through the organization's common model build and distribution infrastructure, shared across all teams.
- Automate the training and release environment with Python, Terraform, AWS SAM/CloudFormation, and GitHub Actions.
- Keep observability of the training and release environment in place, accurate, and useful — primarily through AWS CloudWatch.
- Partner closely with data scientists and with the backend engineers who operate the production service, and take ownership of what you ship — a "you build it, you run it" mentality.
You will thrive if:
- You have strong Python skills for data and automation work, and solid hands-on AWS experience, particularly Lambda, S3, CloudWatch, and IAM.
- You have real data engineering experience — building and operating data pipelines over large datasets, and working with a data lake.
- You bring genuine MLOps practice: versioned model artifacts, model registries, reproducible training runs, and a clear view on how model quality gets measured.
- You're comfortable with infrastructure as code (Terraform, and ideally AWS SAM/CloudFormation), CI/CD with GitHub Actions (or a close equivalent), and containerised workloads with Docker.
- You're fluent in English
- It's a strong plus if you have experience supporting data scientists directly and an instinct for what makes an experimentation environment pleasant to work in; familiarity with feature engineering and common ML frameworks; an understanding of code/model version skew; experience making ML workflows reproducible and auditable; a bias toward deleting complexity rather than adding to it; experience in regulated or certified environments (ISO 9001, SOC 2 Type 2, or similar); and experience working in a global organization with colleagues spread mainly across the EU and North America.
