Senior DevOps – AI, Geospatial & HPC Infrastructure

Mybookinou; Paris; Sur place; EUR 70 000 - 90 000; Plein temps

Résumé du poste

Mybookinou is seeking a Senior DevOps / Platform Engineer to manage cloud infrastructure using AWS and Azure. This role entails designing and maintaining production systems, supporting data-intensive environments, and optimizing geospatial data workflows.

The ideal candidate has 5+ years of experience and strong skills in Docker, Terraform, and CI/CD pipelines. Join a cutting-edge team and contribute to our mission of understanding and valuing forests using advanced technology.

Qualifications

• 5+ years in DevOps / Platform Engineering / Cloud Infrastructure.
• Strong experience with AWS and/or Azure.
• Experience with Docker and containerized environments.
• Solid understanding of CI/CD pipelines.
• Proficient in Linux systems and scripting.

Responsabilités

• Own and operate infrastructure across AWS and Azure.
• Design, deploy, and maintain production-grade systems.
• Support production environments across Python, Node.js, and GraphQL.
• Design and optimize data pipelines for geospatial datasets.

Connaissances

AWS

Azure

Docker

Terraform

CI/CD pipelines

Linux systems

Python

Node.js

PostgreSQL

HPC

Outils

Prometheus

Grafana

Description du poste

Role Overview

At Symbiose, we build the infrastructure layer to understand and value forests at scale using Earth Observation, AI, and High-Performance Computing (HPC). Our platform processes large-scale satellite (Sentinel, LiDAR), geospatial, and climate datasets to produce forest growth models, biomass estimations, and climate risk indicators. We are looking for a Senior DevOps / Platform Engineer to take ownership of the infrastructure powering these systems — across cloud, data pipelines, backend services, and HPC workloads. You will operate a data-intensive, geospatial, and compute-heavy platform in production. This is a unique opportunity to work with state-of-the-art stack.

Key Responsibilities

Infrastructure & Cloud

• Own and operate infrastructure across AWS and Azure
• Design, deploy, and maintain production-grade systems
• Manage Infrastructure as Code (Terraform)
• Ensure security, IAM, networking, and cost control

Backend & Platform Support

• Support production environments across Python, Node.js, and GraphQL services
• Ensure reliability of APIs and backend systems
• Handle asynchronous workloads and batch processing systems

Data & Geospatial Infrastructure

• Design and optimize data pipelines (EO, LiDAR, climate datasets)
• Maintain data lake architectures (S3, Parquet, GeoParquet)
• Optimize PostgreSQL / PostGIS performance
• Support geospatial and raster-heavy workflows (GeoTIFF, COG, MBTiles/PMTiles)

MLOps & Compute Systems

• Enable deployment and scaling of ML models (Python, PyTorch)
• Support training and inference pipelines
• Contribute to model lifecycle and monitoring

HPC & Distributed Workloads

• Operate and optimize HPC / distributed compute environments
• Handle job orchestration, scheduling, and parallel workloads
• Optimize performance, resource allocation, and compute efficiency
• Conduct benchmarking and scaling analysis

Observability & Reliability

• Implement monitoring, logging, and alerting (Prometheus, Grafana)
• Improve fault tolerance and incident response
• Ensure reliability of long-running data and compute jobs

Qualifications

• 5+ years in DevOps / Platform Engineering / Cloud Infrastructure
• Strong experience with AWS and/or Azure (compute, storage, networking)
• Docker (required) and containerized environments
• Terraform (or strong IaC experience)
• CI/CD pipelines (GitLab CI preferred)
• Linux systems and scripting
• Experience supporting data-intensive systems or pipelines
• Comfortable working with Python and Node.js environments
• Ability to work on or quickly adapt to HPC and distributed systems
• Strong ownership mindset and autonomy

Strong Pluses

• Experience with PostgreSQL / PostGIS
• Exposure to geospatial systems or EO data
• Airflow / workflow orchestration
• Spark / PySpark
• MLflow or model tracking systems
• Ray or distributed compute frameworks
• Experience optimizing compute-heavy or batch workloads
• Familiarity with GeoParquet, COG, PMTiles, or large raster pipelines