Key skills
AWSKubernetesAIMLLLMAgenticMLOpsGitOpsCI/CD
About this role
Role Overview
- Build and operate model and inference serving infrastructure
- Own the ML deployment lifecycle
- Operate agentic and LLM workloads in production
- Build reproducible, automated ML pipelines
- Extend infrastructure-as-code to ML systems
- Operate GitOps for ML workloads
- Run ML and AI workloads on multi-tenant Kubernetes
- Own ML reliability and observability
- Drive ML cost efficiency
- Use agentic coding tools for infrastructure and pipeline work
Requirements
- 5+ years in platform engineering, SRE, MLOps, or infrastructure
- Hands-on experience deploying and operating ML or AI workloads in production
- Strong SRE/DevOps foundation
- Deep IaC expertise
- Strong GitOps background
- Deep Kubernetes knowledge
- Strong AWS background
- Hands-on experience building and operating CI/CD pipelines
- Automation-first thinking at a senior level
- Active user of agentic coding tools
- Strong communicator
Tech Stack
- AWS
- Kubernetes
Benefits
- Paid time off
- Flexible work arrangements