R
ML Platform Engineer
Ryan Ashford
Full-time · Mid-level · Chicago
About the role
Role summary: ML Platform Engineer, mid-level. Company: B2B SaaS, 85 employees, Series A. Team: Platform Engineering (4 engineers + 1 manager). Reporting to: Head of Platform. Stack: Python, Kubernetes on AWS EKS, MLflow, Prefect, Docker, PostgreSQL. What this role owns: model serving infrastructure, experiment tracking and model registry, CI/CD pipelines for model deployments, monitoring and alerting for model performance. What this role does not own: model research and training (that's the ML team), product engineering (separate team), data pipelines upstream of model inputs (data engineering). Current state: We run 12 production models. Deployment is semi-automated. MLflow is partially adopted. Monitoring is reactive rather than proactive. Rollback takes too long. Target state: Fully automated deployment with canary releases. MLflow standardised across all teams. Proactive alerting on data drift and performance degradation. Rollback under five minutes. Timeline to target: Two to three quarters. What we need from you: Hands-on engineering capability, clear written communication, and an opinion on the right approach before we've told you what to build.
Responsibilities
- Automate model deployment pipeline from registry trigger to canary release on EKS
- Standardise MLflow adoption across ML and data science teams with clear documentation
- Build proactive monitoring for model performance, data drift, and infrastructure health
- Reduce rollback time from current 40 minutes to under five minutes
- Conduct platform onboarding sessions for new ML team members
Requirements
- 4+ years in platform, DevOps, or MLOps engineering
- Kubernetes — you design and debug pod configurations, resource limits, and scaling behaviour yourself
- MLflow for experiment tracking, model registry, and serving — production deployment experience
- Python for scripting infrastructure, deployment automation, and monitoring integrations
- AWS-native services: EKS, ECR, S3, CloudWatch — not theoretical
- Prefect or Airflow for workflow orchestration
- Docker — you write Dockerfiles and debug image issues without a tutorial
Benefits
- Clear scope, defined target state, and direct path to ownership
- Full remote, US time zones preferred
- $95,000 – $118,000 base salary + equity
- Platform tooling budget: $1,500 quarterly
- $2,000 conference and certification budget annually
Job Type
Full-time
Level
Mid-level
Language
English
Salary Range
$95,000 – $118,000
AI Expertise
MLOps & AI Infrastructure
Ready to apply for this role?
Create a free talent account in under 2 minutes.
- Apply to verified AI companies
- Get AI-matched job recommendations
- Message hiring managers directly
- Build your public AI talent profile