HHAeXchange
Platform Engineer
Rol remoto de Platform Engineering con fit claro de ubicación del candidato.
Publicado2 jul 2026
Países elegibles1 país aceptado
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Estados Unidos
Resumen del rol
Platform Engineer
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
Essential Job Duties
- Own availability, latency, and performance targets for AI platform services and data infrastructure running on AWS
- Design and implement monitoring, alerting, and observability frameworks across the platform stack
- Lead incident response, root cause analysis, and post-mortem processes for platform-level outages or degradations
- Define and track SLOs/SLAs for core platform primitives including RAG pipelines, agent orchestration services, and model access layers
- Proactively identify reliability risks and drive engineering improvements before they become production issues
- Build and maintain runbooks, disaster recovery procedures, and operational documentation
- Design, build, and maintain CI/CD pipelines for AI platform components, data pipelines, and internal applications
- Own infrastructure-as-code (IaC) practices across the team using tools such as Terraform or AWS CDK
- Manage and optimize AWS environments including ECS, Lambda, S3, RDS, Redshift, API Gateway, and related services
- Implement and enforce security, compliance, and cost optimization best practices across AWS infrastructure
- Automate deployment, scaling, and configuration management to reduce manual operational overhead
- Partner with AI Platform Engineers to containerize and operationalize AI services and agent frameworks
- Support Data & AI Engineers with environment management, access controls, and deployment tooling for Polaris and data pipeline infrastructure
- Serve as the operational backbone for the AI platform team – ensuring engineers can ship and iterate quickly without being blocked by infrastructure concerns
- Contribute to our “factory model” vision by making deployment and reliability a repeatable, scalable capability rather than an ad hoc function
Details
- Own availability, latency, and performance targets for AI platform services and data infrastructure running on AWS
- Design and implement monitoring, alerting, and observability frameworks across the platform stack
- Lead incident response, root cause analysis, and post-mortem processes for platform-level outages or degradations
- Define and track SLOs/SLAs for core platform primitives including RAG pipelines, agent orchestration services, and model access layers
- Proactively identify reliability risks and drive engineering improvements before they become production issues
- Build and maintain runbooks, disaster recovery procedures, and operational documentation
- Design, build, and maintain CI/CD pipelines for AI platform components, data pipelines, and internal applications
- Own infrastructure-as-code (IaC) practices across the team using tools such as Terraform or AWS CDK
- Manage and optimize AWS environments including ECS, Lambda, S3, RDS, Redshift, API Gateway, and related services
- Implement and enforce security, compliance, and cost optimization best practices across AWS infrastructure
- Automate deployment, scaling, and configuration management to reduce manual operational overhead
- Partner with AI Platform Engineers to containerize and operationalize AI services and agent frameworks
- Support Data & AI Engineers with environment management, access controls, and deployment tooling for Polaris and data pipeline infrastructure
- Serve as the operational backbone for the AI platform team – ensuring engineers can ship and iterate quickly without being blocked by infrastructure concerns
- Contribute to our “factory model” vision by making deployment and reliability a repeatable, scalable capability rather than an ad hoc function
- Design, build, and maintain CI/CD pipelines for AI platform components, data pipelines, and internal applications
- Own infrastructure-as-code (IaC) practices across the team using tools such as Terraform or AWS CDK
- Manage and optimize AWS environments including ECS, Lambda, S3, RDS, Redshift, API Gateway, and related services
- Implement and enforce security, compliance, and cost optimization best practices across AWS infrastructure
- Automate deployment, scaling, and configuration management to reduce manual operational overhead
- Partner with AI Platform Engineers to containerize and operationalize AI services and agent frameworks
- Support Data & AI Engineers with environment management, access controls, and deployment tooling for Polaris and data pipeline infrastructure
- Serve as the operational backbone for the AI platform team – ensuring engineers can ship and iterate quickly without being blocked by infrastructure concerns
- Contribute to our “factory model” vision by making deployment and reliability a repeatable, scalable capability rather than an ad hoc function
- Serve as the operational backbone for the AI platform team – ensuring engineers can ship and iterate quickly without being blocked by infrastructure concerns
- Contribute to our “factory model” vision by making deployment and reliability a repeatable, scalable capability rather than an ad hoc function
- 3+ years of professional experience in a DevOps, SRE, or platform engineering role
- Hands-on AWS experience required – AgentCore, Bedrock, ECS, Lambda, S3, RDS, Redshift, CloudWatch, IAM, VPC, and related services
- Experience with infrastructure-as-code tools such as Terraform or AWS CDK
- Strong CI/CD experience with tools such as GitHub Actions
- Experience with containerization and orchestration (Docker, ECS, or Kubernetes)
- Familiarity with AI/ML infrastructure patterns – model serving, vector databases, pipeline orchestration (strongly preferred)
- Experience with observability and monitoring tooling (Datadog, CloudWatch)
- Prior experience in a SaaS environment
- Strong verbal and written communication skills with ability to collaborate across technical and non-technical stakeholders
- Self-starter with a proactive approach to identifying and resolving infrastructure risk before it impacts delivery
- Willingness to explore and adopt AI tools responsibly to enhance productivity and innovation in your role.
Other Job Duties
- Other duties as assigned by supervisor or HHAeXchange leader.
Travel Requirements
- Travel up to 10%, including overnight travel
Required Education, Experience, Certifications and Skills
- 3+ years of professional experience in a DevOps, SRE, or platform engineering role
- Hands-on AWS experience required – AgentCore, Bedrock, ECS, Lambda, S3, RDS, Redshift, CloudWatch, IAM, VPC, and related services
- Experience with infrastructure-as-code tools such as Terraform or AWS CDK
- Strong CI/CD experience with tools such as GitHub Actions
- Experience with containerization and orchestration (Docker, ECS, or Kubernetes)
- Familiarity with AI/ML infrastructure patterns – model serving, vector databases, pipeline orchestration (strongly preferred)
- Experience with observability and monitoring tooling (Datadog, CloudWatch)
- Prior experience in a SaaS environment
- Strong verbal and written communication skills with ability to collaborate across technical and non-technical stakeholders
- Self-starter with a proactive approach to identifying and resolving infrastructure risk before it impacts delivery
- Willingness to explore and adopt AI tools responsibly to enhance productivity and innovation in your role.
Roles similares
Mantén una lista de respaldo.
AWS, CI/CD 13 países aceptados
Senior QA Automation EngineerSubway EcommerceVer rol AWS, Kubernetes 13 países aceptados
Senior Backend Engineer (AdTech)Leap ToolsVer rol AWS, Kubernetes 13 países aceptados
Senior Backend EngineerLeap ToolsVer rol AWS 13 países aceptados
Senior Software EngineerBaltimore BannerVer rol Stack
Usa estas tags para comparar roles remotos similares.
Elegibilidad de ubicación
Candidatos deberían aplicar solo cuando el país del perfil aparece aquí.
Tu perfilPaís no definidoInicia sesión para comparar tu país con este rol.
Flujo de contratación
WithMira muestra el rol y luego envía candidatos a la aplicación de la empresa.
1Revisa fit del rol, stack y elegibilidad de ubicación en WithMira.
2Abre la página de aplicación de la empresa desde el link rastreado.
3Guarda el rol o suscríbete a oportunidades similares antes de salir.