Principal Forward Deployed Engineer: AI Infra & CloudNative

Socket.dev

Colorado

Hybrid

USD 132,000 - 212,000

Full time

6 days ago
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Job summary

Socket.dev is seeking a Forward Deployed Engineer (Principal) to act as a technical authority and field engineering leader across the Infrastructure Software Division. You will embed with our largest enterprise accounts during platform modernizations and AI inference deployments.

As a Principal, you will influence roadmaps, author reference implementations, and mentor engineers, while eliminating adoption friction and codifying deployment patterns for cloud-native workloads across multi-cloud

Qualifications

  • 10+ years in software engineering with 5+ years in infrastructure or Kubernetes at scale.
  • Proven ability to command technical credibility with C-suite while remaining hands-on in production code.
  • Deep knowledge of Kubernetes primitives, CRDs, Operators, and GitOps architectures.

Responsibilities

  • Bridge core engineering, product management, and enterprise customer architects.
  • Architect and validate production-grade Kubernetes topologies and cloud-native platforms.
  • Design and deploy scalable AI inference pipelines and distributed runtimes.
  • Lead end-to-end migration strategies for legacy systems to cloud-native targets.
  • Contribute code and upstream improvements to core ISG platforms.
  • Influence product roadmaps by translating customer pain points into features.

Skills

Kubernetes
CRDs & Operators
GitOps
Go
Python
Java
Distributed systems
AI inference
GPU infrastructure
VKS/VCF architectures

Education

Bachelor's degree

Tools

ArgoCD
Flux
Kubernetes
Cluster API (CAPI)
OpenShift

Job description

Socket.dev is seeking a Forward Deployed Engineer (Principal) to act as a technical authority and field engineering leader across the Infrastructure Software Division. You will embed with our largest enterprise accounts during platform modernizations and AI inference deployments.

As a Principal, you will influence roadmaps, author reference implementations, and mentor engineers, while eliminating adoption friction and codifying deployment patterns for cloud-native workloads across multi-cloud

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