Senior Infrastructure Engineer, AI/ML Systems
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Description
- Build and operate deployment pipelines for AI services
- Deploy and operate model serving, batch scoring, and orchestration pipelines
- Partner with data scientists and applied AI engineers for system design and production deployment
- Own production reliability for AI services including monitoring, alerting, debugging, performance, and cost
- Spot repeated patterns and create reusable templates
- Six or more years in infrastructure, DevOps, platform, or ML engineering with production system ownership
- Deep hands-on experience with 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 optionally Kubernetes or ECS/EKS
- Experience with orchestration and workflow tooling such as Airflow, Dagster, Argo, or Step Functions
- Comfort working through ambiguity and collaborating 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
- Generous time off including local holidays and annual “Dim the Lights” period
- Comprehensive wellness programs and mental health support
- Learning and development resources including professional development tools and tuition reimbursement
- Provision of technology and tools needed for work
- Motivosity employee recognition program
- Culture rooted in inclusivity, support, and meaningful connection