Deskripsi Pekerjaan
Apex Neural Dynamics is seeking an exceptional Staff Platform Engineer to lead the evolution of our cloud infrastructure and developer platform. You will design, scale, and maintain resilient distributed systems supporting cutting-edge machine learning inference workloads. This high-impact role blends foundational systems architecture, automation, and technical mentorship across global engineering teams.
Tanggung Jawab
- Architect, deploy, and operate multi-region Kubernetes clusters optimized for low-latency machine learning payloads.
- Drive platform reliability through automated self-healing mechanisms, canary release pipelines, and fault-tolerant infrastructure patterns.
- Standardize infrastructure as code across all environments using Terraform, OpenTofu, and GitOps workflows.
- Instrument deep observability frameworks spanning metrics, distributed tracing, and log aggregation using Prometheus and OpenTelemetry.
- Partner with product security teams to enforce zero-trust network boundaries, secrets management, and compliance controls.
- Lead root-cause analysis investigations for high-severity incidents, translating learnings into structural platform mitigations.
- Mentor senior engineers, establishing system engineering best practices and driving technical roadmaps across business units.
Kualifikasi
- 8+ years of production experience managing large-scale distributed systems and cloud-native architecture in AWS or GCP.
- Expert-level proficiency in container orchestration with Kubernetes, including custom controllers, operators, and service meshes.
- Strong development experience in Go, Python, or Rust for systems programming and tooling development.
- Demonstrated track record designing Infrastructure as Code using Terraform or Pulumi at enterprise scale.
- In-depth knowledge of Linux internals, network protocols (TCP/IP, HTTP/2, gRPC), and distributed storage systems.
- Hands-on experience architecting high-throughput CI/CD release platforms and automated validation pipelines.
- Bachelor's or Master's degree in Computer Science, related technical field, or equivalent practical industry experience.