Top Tech Jobs & Startup Jobs

Develop Kubernetes-native AI infrastructure using Go, including inference and training products, controllers, operators, APIs, CLIs, and developer tools. Optimize serverless container workflows, GPU scheduling, workload scaling, reliability, security, and distributed systems performance. Collaborate on cloud-native technologies and Kubernetes ecosystem advancements. Required experience includes Go, Kubernetes development, Kubernetes APIs and CRDs, Docker, Helm, and cloud-native architecture; Python, AI/ML frameworks, GPU optimization, and open-source contributions are preferred.

Troubleshoot and proactively maintain server hardware in data centers, diagnose issues, collaborate with vendors and data center personnel, document configurations and resolutions, and improve hardware maintenance processes. The role also supports GPU clusters, networking infrastructure, spare-parts optimization, and potential automation using Bash, Python, and AI.

Build and maintain software for network configuration, monitoring, validation, testing, and automation. Integrate network automation into engineering deployment pipelines and automate software development, release, and operations. Support Linux environments and containerized network services, monitor infrastructure health, research automation technologies, and improve the reliability and scalability of mission-critical systems.

Owns infrastructure projects end to end, including data center build-outs, hardware deployments, connectivity procurement, migrations, budgeting, risk management, vendor coordination, installation, operational handover, and closure. The role manages parallel workstreams across architecture, procurement, logistics, engineering, and operations while maintaining Jira workflows, Kanban metrics, schedules, dependencies, and stakeholder communications.

Build and optimize Gcore’s AI inference platform using Python, PyTorch, inference frameworks, GPUs, and Kubernetes. Deploy language and multimodal models, improve latency, throughput, memory efficiency, GPU utilization, reliability, and cost, and debug issues across software, infrastructure, hardware, networking, and distributed systems. Collaborate with platform, infrastructure, product, and customer-facing teams, while contributing to open-source inference projects.

Lead four engineering teams responsible for Gcore’s CDN control plane, edge and proxy layer, analytics and logging platform, DNS, and traffic management services. Own delivery planning, technical prioritization, incident response, on-call operations, service quality, hiring, and engineer development. The role requires strong understanding of internet infrastructure, distributed systems, HTTP caching, nginx, TLS, SNI, and anycast routing, plus experience managing distributed teams and driving post-incident improvements.

Design, build, and maintain a Go-based Traffic Management System API and edge agent to render/apply routing configuration. Implement routing (BGP/Anycast), automated failover, high-availability features, and strengthen reliability via tests, metrics, and observability while collaborating with cross-functional teams.

Design and build a managed Slurm service on Kubernetes, writing production-grade Go code for GPU-intensive workload scheduling and orchestration. Develop observability and automated remediation for GPU, node, network, and control-plane failures. Diagnose performance and reliability issues across HPC infrastructure, distributed storage, high-performance networks, and schedulers while preserving Slurm behavior and delivering a customer-focused platform.

Build and operate Python services and APIs for GPU virtual machines and bare-metal infrastructure. Develop provisioning, placement, maintenance, recovery, capacity, and scheduling workflows; integrate GPU servers with OpenStack and Kubernetes; automate validation and production readiness; and improve testing, observability, deployment safety, and operational tooling. Investigate complex production issues across compute, networking, storage, virtualization, and hardware while owning projects through rollout and ongoing operation.

Design, deploy, and maintain on-premises infrastructure for scalable AI inference workloads. Build GPU scheduling and model deployment pipelines, manage monitoring and observability systems, troubleshoot Kubernetes, Linux, networking, and production infrastructure, and collaborate with ML and platform teams on architecture and performance testing.