R
Cloud ML Engineer
Rafael Santos
Full-time · Mid-level · New_York
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.
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
Benefits
- Greenfield MLOps build — you design the standards, not inherit them
- Full remote
- $105,000 – $135,000 base salary + equity
- $2,000 annual cloud certification and learning budget
- Flexible hours with core overlap 10am–3pm US Eastern
Job Type
Full-time
Level
Mid-level
Language
English
Salary Range
$105,000 – $135,000
AI Expertise
MLOps & AI Infrastructure
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