Skills

Full 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

Senior Infrastructure Engineer, AI/ML Systems

Compensation

Not disclosed

Location

Not listed

Work setup

Remote

Seniority

Senior

Employment

Not specified

Experience

Not specified

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