Senior MLOps / ML Platform Engineer

Black Swans Exist GmbH; España; Remote; EUR 90,000 - 140,000; Full time

Job summary

Black Swans Exist GmbH, a specialist life sciences software company building a SaaS platform for biopharma and scientific teams, is seeking a Senior MLOps / ML Platform Engineer. You will own the platform behind AI models, focusing on reproducibility, traceability and scalable workflows across experiments and production.

This remote, Europe/UK based role collaborates with scientists and engineers to design reliable platform capabilities, orchestrate ML workflows with Flyte or Airflow, manage

Qualifications

• 5+ years' experience in MLOps, ML platform, data platform or platform engineering.
• Hands-on experience building platforms that other teams rely on.
• Strong Python skills and production engineering practices.
• Experience with Kubernetes, Docker and CI/CD.
• Experience with workflow orchestration and experiment tracking.

Responsibilities

• Own the ML platform architecture and tooling standards used by other teams.
• Build and orchestrate ML workflows using Flyte, Airflow or similar tools.
• Run experiment tracking, model registries and artifact management (MLflow).
• Ensure reproducibility and lineage of models and runs as the platform evolves.
• Validate and monitor model behavior in production.
• Manage Kubernetes, Docker and cloud infra behind training and inference workloads.
• Build CI/CD for ML and data pipelines.
• Improve observability, governance and reliability of the platform.

Skills

MLOps

ML platform

Python

Kubernetes

Docker

CI/CD

Workflow orchestration

Experiment tracking

Data lineage

Data pipelines

GxP exposure

Tools

Flyte

MLflow

DVC

Kubeflow

Airflow

Spark

Terraform

Job description

Remote – Europe / UK based

We are supporting a search for a Senior MLOps / ML Platform Engineer within a specialist life sciences software company that is building a SaaS platform for biopharma and scientific teams.

This is a platform role rather than a model-building one. The company's product relies on AI and scientific models, and you would own the platform that sits behind them, from how workflows are run and experiments are tracked to how every result can be reproduced and trusted long after it was first produced.

The scientists and modellers are effectively your users, so you would work closely with them as well as with backend, data and infrastructure engineers to make complex ML workflows reliable, traceable and easy to run at scale.

It would suit an engineer who naturally thinks long-term, someone who will fix today's problem but then design it out properly so it doesn't come back six months later.

Role focus

• Owning the ML platform architecture and setting the standards and tooling that other teams build on
• Building and orchestrating ML workflows using Flyte, Airflow or similar tools
• Running experiment tracking, model registries and artifact management, for example with MLflow
• Making sure reproducibility and lineage hold up, so older models and runs remain usable as the platform evolves
• Validating and monitoring model behaviour once models are in production
• Managing the Kubernetes, Docker and cloud infrastructure behind training and inference workloads
• Building CI/CD for both ML and data pipelines
• Improving observability, governance and reliability across the whole platform
• Working with scientists and engineers to turn their recurring needs into proper platform capabilities

What we are looking for

• 5+ years' experience in MLOps, ML platform, data platform or platform engineering
• A software engineering mindset first and foremost, combined with genuine curiosity about the science behind the product
• Hands-on experience building platforms that other teams rely on, rather than only working on top of them
• Strong Python skills and solid production engineering practices
• Practical experience with Kubernetes, Docker and CI/CD
• Experience with workflow orchestration and experiment tracking
• A good understanding of how data behaves, including pipelines, quality, versioning and lineage
• Enough ML knowledge to work closely and confidently with model developers
• Comfortable making decisions, stating your assumptions and explaining trade-offs clearly
• Motivated by platform work itself, and keen to keep growing in this direction long term
• Experience with Flyte, MLflow, DVC, Kubeflow or similar tools
• Terraform or other infrastructure-as-code tools
• Data engineering experience with Spark, Airflow, dbt or streaming
• Working with large, complex or scientific datasets
• Exposure to biotech, pharma or other regulated environments, with GxP a plus

This isn't a generic DevOps role with ML added to the title, and it isn't a data science role either. The company needs someone who can own the platform foundation behind models used in a regulated scientific environment, and who wants to shape it as the product grows.