Key skills
DockerKubernetesTerraformMLMLOpsCI/CD
About this role
Role Overview
- own the production runtime for Phare’s ML stack, deploying, serving, and scaling models
- build progressive delivery pipelines with automated rollouts and rollbacks
- manage SLOs for latency and availability
- instrument end-to-end observability including metrics, logs, traces, drift, regression
- harden the platform with Terraform, Kubernetes, and CI/CD
Requirements
- at least 5 years of relevant industry experience in software engineering
- at least 2 years of direct MLOps experience
- experience in deploying and operating models running on GPUs in production
- strong with Docker/Kubernetes, IaC (e.g., Terraform), and CI/CD for services and model artifacts
- use progressive delivery with automated rollouts/rollbacks
- manage model registries and stage gates
- experience in regulated environments (e.g., healthcare, finance) is a bonus
Tech Stack
- Docker
- Kubernetes
- Terraform
Benefits
- competitive benefits package
- annual bonus plan at a target of 40%