Cloud ML Engineer

Ponto AI

Remote · $105K – $135K · Full-time · Posted 6 months ago

This role is no longer accepting applications

Don't miss the next role like this

Create a free profile with your skills. We match you to new AI roles as they open, and companies hiring can contact you directly.

Create free profile, 2 minutes

Similar open roles you can apply to now:

See all open AI jobs

This role is no longer accepting applications

Don't miss the next role like this

Create a free profile with your skills. We match you to new AI roles as they open, and companies hiring can contact you directly.

Create free profile, 2 minutes

Similar open roles you can apply to now:

See all open AI jobs

About the role

We're moving our ML infrastructure from a setup that was fine when we had three data scientists to one that can support ten and scale to twenty. Right now models are trained on EC2 instances, experiments are tracked in a shared spreadsheet, and deployment is a script someone runs manually from their terminal. We know this is not sustainable. We need an engineer who has done this migration before — containerised model serving, proper experiment tracking with MLflow, a model registry, automated deployment pipelines. AWS is our primary environment and we're comfortable staying there. We use GCP for some BigQuery workloads that won't move. Kubernetes for orchestration. We are not looking to adopt every new tool — we care about reliability and documentation more than novelty. If you are the kind of engineer who writes runbooks, keeps architecture diagrams up to date, and gets annoyed by undocumented infrastructure, we are going to get along extremely well.

Fully remote.

Responsibilities

Design and implement the transition from manual deployment to an automated ML pipeline
Set up and standardise MLflow experiment tracking across all data science projects
Build and maintain Kubernetes-based model serving infrastructure on AWS EKS
Create and maintain runbooks, architecture diagrams, and deployment documentation
Work with data scientists to ensure their code is production-ready before handoff

Requirements

4+ years in cloud infrastructure or MLOps
AWS-native expertise — EKS, S3, ECR, CloudWatch, and at least one managed training service
Docker and Kubernetes for containerised model serving and scaling
MLflow for experiment tracking, model versioning, and serving
GCP familiarity (BigQuery, Cloud Run) is a real bonus
Strong Python for scripting infrastructure and deployment automation
Opinionated about documentation — you leave things better than you found them