Context
Within a bank, you join a Data department whose team supports business lines on their Data Science projects, from use-case identification through to production deployment. The team is international and works fairly autonomously within a large group. It includes Data Scientists, an ML Engineering unit, and an AI projects team.
It develops in-house a Data Science platform for Data Analysts and Data Scientists. This platform includes:
•a Python exploration and development environment,
•a model industrialization framework and an internal library,
•a platform for running models in production,
•a tool for animating the user community.
It automatically provisions work environments to build and train models, perform analyses, and develop AI agents. The platform has more than 500 users and about 400 containers in production.
Missions
Ops – Infrastructure and operations
•Administer, operate and automate the operating systems of the clusters
•Build and optimize Docker images, and ensure the run of production containers
•Carry the technical vision of the infrastructure and participate in technology choices and architecture evolution
•Optimize resource sizing and associated costs
•Be the main point of contact for the group's infrastructure teams, which provide the on-premise managed services used by the platform
•Keep components up to date and contribute to application deployments
•Participate in the evolution of CI/CD pipelines (GitLab CI, GitOps) and observability (logs, monitoring, traceability)
•Write technical documentation: architecture, change management and incident management
•Ensure technology watch and participate in POCs
User support
•Be the first point of contact for platform users and support them in using it
•Analyze and resolve their difficulties
•Provide level 1 support on incidents, with the goal of resolving at least two-thirds of tickets at this level
•Set up and evolve support tools and processes
•Communicate with users about production releases, service interruptions and upcoming changes
•Gather user needs and propose platform improvements
Technical environment
•Languages: Python, Go, Bash
•System: GNU/Linux
•Containers: Docker, Kubernetes
•DevOps: GitLab, GitLab CI, ArgoCD, Helm, Kustomize, CUE/Timoni, Buildkit, Artifactory
•Observability: Prometheus, Thanos, AlertManager, Grafana, ELK, Jaeger
•Security and network: Vault, Traefik, Keycloak (OIDC, JWT), Cert-manager
•Data and storage: PostgreSQL, ClickHouse, Redis, MinIO (S3), Portworx
•Private on-premise cloud
Working conditions
•Mission location: Paris
•Start: ASAP
•Long-term mission
•2 days of remote work
•Professional English required
•Required seniority: 8 to 10 years of experience minimum
Technology specialties
Artifactory, Bash, CI/CD, ClickHouse, Docker, ELK, GitLab, GitLab CI, GitOps, Grafana, Helm, Keycloak, Kubernetes, Kustomize, PostgreSQL, Prometheus, Python, Redis, SQL
Profile sought
•Solid skills in systems engineering or DevOps
•Good command of Kubernetes, including cluster architecture and administration
•Good knowledge of GitLab and CI/CD practices, as well as the GitOps approach (ArgoCD)
•Observability in a Kubernetes context (Prometheus, AlertManager, Grafana)
•Network and security knowledge: reverse proxy (Traefik), authentication (Keycloak, OIDC, JWT), secrets management (Vault)
•Programming in Python and SQL
•Some notions of Data Engineering or Data Science, sufficient to understand user usages
•Minimum 8 years of experience setting up and operating support tools and processes
•User orientation and service mindset
•High degree of autonomy, particularly in finding solutions independently
•Rigor, organization and proactivity
•Ability to prioritize and run several topics in parallel
•Team spirit
•Good communication in French and English
•Appreciated: minimum 8 years of experience on ML or AI topics
•Appreciated: PostgreSQL
•Appreciated: ELK, ReactJS
