Founding Infra Engineer

This role is for one of Weekday’s clients

Min Experience: 4+ years

Location: Gurugram, Haryana, India, Gurgaon, Haryana, India

JobType: full-time

We are looking for a highly skilled and entrepreneurial Founding Infrastructure Engineer with 4-10 years of experience to build, scale, and own the infrastructure powering our next generation of products and AI workloads. This is an early engineering role with significant ownership, where you will work closely with the founding team to design infrastructure from the ground up and establish systems that are reliable, scalable, secure, and cost-efficient.

The ideal candidate has strong hands-on expertise in infrastructure, GPU computing, and Kubernetes, with a deep understanding of distributed systems and cloud-native technologies. You should be comfortable operating in an ambiguous, fast-paced environment and taking projects from architecture and design through implementation and production operations.

Requirements

Key Responsibilities

• Design, build, and operate highly available and scalable infrastructure for production and compute-intensive workloads.
• Architect and manage GPU infrastructure for machine learning, AI, and other high-performance computing workloads.
• Design, deploy, and maintain Kubernetes clusters across cloud and/or on-premise environments.
• Optimize GPU utilization, scheduling, networking, storage, and compute resources to maximize performance and cost efficiency.
• Build infrastructure automation using Infrastructure as Code and modern DevOps practices.
• Establish reliable deployment, monitoring, observability, alerting, and incident-response systems.
• Develop scalable solutions for container orchestration, workload scheduling, resource allocation, and service discovery.
• Manage infrastructure lifecycle, including provisioning, upgrades, capacity planning, performance tuning, and disaster recovery.
• Work closely with application and ML engineers to provide reliable infrastructure for model training, inference, experimentation, and production services.
• Identify infrastructure bottlenecks and proactively improve system reliability, performance, scalability, and security.
• Define engineering best practices around infrastructure architecture, Kubernetes operations, CI/CD, and production readiness.
• Participate in technical strategy and help shape the infrastructure roadmap as an early member of the engineering team.

Must-Have Skills

• 4-10 years of hands-on experience in infrastructure engineering, platform engineering, DevOps, or SRE.
• Strong expertise in infrastructure architecture and operations across production environments.
• Deep hands-on experience with Kubernetes, including cluster architecture, deployments, networking, storage, scheduling, and troubleshooting.
• Strong understanding of GPU infrastructure, GPU provisioning, utilization, scheduling, and performance optimization.
• Experience working with containerization technologies such as Docker and Kubernetes-based workloads.
• Strong understanding of Linux systems, networking, compute, storage, and distributed systems.
• Experience with cloud infrastructure and services, preferably AWS, GCP, or Azure.
• Proficiency with Infrastructure as Code tools such as Terraform and configuration-management/automation tools.
• Experience building CI/CD pipelines and automated infrastructure workflows.
• Strong debugging and problem-solving skills across complex production environments.

Good-to-Have Skills

• Experience with NVIDIA GPUs, CUDA, GPU operators, or GPU orchestration.
• Experience managing large-scale GPU clusters or AI/ML infrastructure.
• Knowledge of Kubernetes operators, Helm, service meshes, and cluster autoscaling.
• Experience with high-performance networking, distributed storage, and workload schedulers.
• Exposure to ML platforms, model serving, inference infrastructure, or large-scale training systems.
• Experience with observability tools such as Prometheus, Grafana, OpenTelemetry, or similar technologies.
• Experience building infrastructure at an early-stage startup or as an early engineering hire.