Senior Platform Engineer: AI Infra, CI/CD & Self-Service

Archer; San Jose (CA); Presencial; USD 144 000 - 175 000; Tempo integral

Resumo da oferta

Archer, a San Jose-based aerospace company, seeks a Senior Platform Engineer to accelerate product development through automation and self-service infrastructure. You will bridge development, infrastructure, and AI/ML initiatives, focusing on scalable CI/CD and workflow optimization.

Collaborate with AI/ML engineers to operationalize MLOps pipelines, optimize resource usage (GPUs and specialized hardware), and build internal platforms that empower engineering teams to move fast and securely.

Qualificações

• 5+ years in Platform Engineering, DevOps, or SRE.
• Deep cloud expertise and IaC tooling (Terraform preferred).
• Containerization and orchestration with Docker and Kubernetes.
• Scripting and general-purpose languages: Python, Go, Bash.
• Extensive experience designing and managing CI/CD at scale.
• Proven track record building internal developer platforms.

Responsabilidades

• Design, implement, and maintain scalable CI/CD pipelines across environments.
• Drive best practices for build management, testing automation, and deployment strategies.
• Minimize deployment friction and cycle time for engineering teams.
• Develop self-service tooling and internal platforms for provisioning infrastructure.
• Establish guardrails and policy-as-code for compliance and security.
• Collaborate with AI/ML teams to improve models platform and MLOps pipelines.
• Architect automated workflows and internal APIs to connect engineering systems.

Conhecimentos

5+ years of experience

Cloud platforms (AWS GCP Azure)

Scripting languages (Python, Go, Bash)

CI/CD at scale

Internal developer platforms

Ferramentas

Terraform

Docker

Kubernetes

Descrição da oferta de emprego

Archer, a San Jose-based aerospace company, seeks a Senior Platform Engineer to accelerate product development through automation and self-service infrastructure. You will bridge development, infrastructure, and AI/ML initiatives, focusing on scalable CI/CD and workflow optimization.

Collaborate with AI/ML engineers to operationalize MLOps pipelines, optimize resource usage (GPUs and specialized hardware), and build internal platforms that empower engineering teams to move fast and securely.