Senior Infrastructure Engineer, AI/ML Systems
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Summary
Builds and operates the production infrastructure for AI/ML services: CI/CD pipelines, infrastructure-as-code, model serving, batch scoring, and workflow orchestration on AWS. Owns reliability, monitoring, performance, and cost, partnering with data scientists to move prototypes into production.
- Build and operate deployment pipelines for AI services covering CI/CD, infrastructure-as-code, environment promotion, and rollback
- Deploy and operate model serving, batch scoring, and orchestration pipelines across development, staging, and production
- Partner with data scientists and applied AI engineers to take prototypes into production including system design for net-new services
- Own production reliability for AI services including monitoring, alerting, debugging, performance, and cost
- Spot repeated patterns and turn them into reusable templates
- Six or more years in infrastructure, DevOps, platform, or ML engineering with ownership of systems running in production
- Deep hands-on experience across a wide range of AWS services including compute, networking, storage, deployment, and monitoring
- Infrastructure-as-code experience with Terraform, CDK, or CloudFormation
- Experience with containers and modern deployment patterns including Docker and Kubernetes or ECS/EKS applied to CI/CD pipelines designed and operated for production services
- Experience with orchestration and workflow tooling such as Airflow, Dagster, Argo, Step Functions, or similar
- Comfort working through ambiguity and collaborating directly with data scientists and researchers
- Experience with ML platform components and data pipeline orchestration at scale
- Experience running LLM-based or retrieval-based systems in production
- Experience operating specialized data stores including graph databases
- Experience building internal tooling, templates, or reference implementations adopted by other engineers
- Competitive compensation including participation in ownership program
- Flexible work culture with remote, hybrid, and in-office collaboration spaces varying by role, team, and location
- Generous time off including local holidays and annual “Dim the Lights” period in late December
- Comprehensive wellness programs and mental health support
- Learning and development resources including professional development tools and tuition reimbursement
- Technology and tools needed to do best work
- Motivosity employee recognition program
- A culture rooted in inclusivity, support, and meaningful connection