Platform Engineer (Barcelona)

United Kingdom·CambourneRemotemid

EngineeringDevops Engineer

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Quick Summary

Key Responsibilities

manifest schemas and validation, catalogs and versioning, install/upgrade/remove lifecycles, configuration and secrets handling, health reporting,

Requirements Summary

you have owned backend services end to end, including API design, data models, testing, and packaging, not only automation scripts.

Technical Tools

EngineeringDevops Engineer

Responsibilities

~1 min read

→Design, build, and maintain the Python services through which external applications, engines, and infrastructure are integrated and managed inside Foundry: manifest schemas and validation, catalogs and versioning, install/upgrade/remove lifecycles, configuration and secrets handling, health reporting, and the REST APIs that expose them to the Foundry UI and CLI.
→Define the integration contract that lets an external engine plug into Foundry's IAM, metadata layer, and UI once, and drive the first integrations through it.
→Own Foundry's deployment packaging (Helm charts, operators, offline bundles) and keep the platform installable, upgradable, and supportable across Kubernetes distributions, cloud providers, sovereign clouds, and air-gapped environments.
→Integrate Foundry with customer-side infrastructure: OIDC/SAML identity providers, object storage, container registries, ingress and network policy, and GPU scheduling.
→Own the CI/CD pipelines, release process, and multi-environment integration test matrix that validate every supported deployment target.
→Instrument the platform and installed apps with metrics, logs, and traces, and write the runbooks needed to operate and support production deployments.
→Turn prototypes and one-off integrations into stable, documented, versioned services and tooling that other teams and customers can rely on.

Requirements

~2 min read

5+ years in platform, infrastructure, or backend engineering, with at least 2 years shipping and operating production workloads on Kubernetes.
3+ years writing production Python: you have owned backend services end to end, including API design, data models, testing, and packaging, not only automation scripts.
Experience building and maintaining REST APIs in a modern Python framework (FastAPI, Django REST Framework, or similar), including versioning, authentication, and input validation (Pydantic or equivalent).
Experience with Python data access and migrations (SQLAlchemy, Alembic, or equivalent) on PostgreSQL, and with schema design.
Comfortable with async Python, structured logging, and writing testable code with Pytest; you use type hints and linters as a matter of course.
Experience interacting with Kubernetes and cloud APIs programmatically from Python (Kubernetes client, Boto3, or equivalent), for example building installers, controllers, or operational tooling.
Deep, hands-on experience with Helm and Kubernetes packaging; you have written and maintained charts that other people install.
Experience deploying software into environments you do not control: on-premises, private cloud, or restricted-network/air-gapped installs.
Experience with AWS: EKS, ECR, RDS, S3, Secrets Manager.
Experience integrating with enterprise identity (OIDC, OAuth2, SAML) and managing secrets and configuration in production.
Solid understanding of Docker, image building and hardening, and container registries.
Proficiency with Git and CI/CD pipelines (GitLab CI or GitHub Actions).
A product-oriented mindset: able to turn R&D scripts or prototypes into stable, usable services.

Experience writing Kubernetes operators or controllers (in Python with kopf, or in Go), or with GitOps tooling (ArgoCD, Flux).
Experience building plugin, extension, or app-store style systems: manifest formats, lifecycle hooks, dependency and version resolution.
Experience publishing Python packages or CLIs that others install (packaging, versioning, backwards compatibility).
Experience with GPU workloads on Kubernetes (device plugins, node scheduling, NVIDIA GPU Operator) and with LLM serving tools (vLLM, Triton, NIM).
Familiarity with observability tooling (Prometheus, Grafana, OpenTelemetry) and with exposing it for third-party components.
Exposure to ML orchestration tooling (Flyte, Airflow, MLflow, SkyPilot).
Go, and a track record of contributing to open-source infrastructure projects.