Hardware Engineer

aion

Hardware Engineer designing and integrating infrastructure optimized for AI workloads at aion. Collaborating with global teams to build production-ready AI systems and manage complex challenges.

Posted 7/22/2026full-timeBengaluru • 🇮🇳 IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing hardware platforms for AI workloads, with a strong focus on server architecture, performance benchmarking, and hardware lifecycle management. Proficient in collaborating with cross-functional teams to enhance system reliability and operational efficiency.

Highest-signal resume keywords

Hardware EngineeringAI Infrastructure OptimizationPerformance BenchmarkingHardware ValidationInfrastructure Lifecycle Management

ATS Keywords

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Hard Skills

Server ArchitectureNVIDIA GPUsAMD AcceleratorsPCIeNVLinkInfiniBandLinux SystemsPython ScriptingHardware DiagnosticsThermal Design

Soft Skills

CollaborationProblem-SolvingCommunication

Tools & Technologies

KubernetesVirtualizationInfrastructure-as-CodeMonitoring ToolsHigh-Speed Interconnects

Industry Keywords

Data Center InfrastructureCloud InfrastructureHybrid CloudAI WorkloadsOperational Excellence

Tech Stack

Tools & technologies

CloudKubernetesLinuxPython

About the role

Key responsibilities & impact

Design and build hardware platforms optimized for AI training and inference workloads.
Evaluate, integrate, and validate servers, GPUs, networking equipment, storage systems, and accelerator hardware.
Develop scalable hardware architectures supporting enterprise AI deployments across cloud, hybrid, and on-premises environments.
Collaborate with software and platform engineering teams to optimize hardware and software integration.
Optimize compute, memory, storage, networking, and GPU performance for AI workloads.
Benchmark and analyze system performance under production-scale AI deployments.
Identify hardware bottlenecks and implement improvements to maximize throughput, latency, and infrastructure efficiency.
Validate hardware compatibility across multiple AI frameworks and deployment environments.
Build reliable and fault-tolerant hardware infrastructure capable of supporting mission-critical AI workloads.
Develop hardware validation, diagnostics, stress testing, and failure analysis processes.
Support hardware lifecycle management including provisioning, upgrades, maintenance, and replacement strategies.
Collaborate with vendors and partners to evaluate emerging hardware technologies.
Support hardware deployment across enterprise customer environments and internal infrastructure.
Develop automation and operational procedures for hardware provisioning, monitoring, and troubleshooting.
Ensure infrastructure meets enterprise standards for availability, security, and operational excellence.
Work closely with customer-facing teams to resolve deployment and infrastructure challenges.
Establish best practices for hardware validation, documentation, testing, and operational readiness.
Conduct technical reviews and contribute to infrastructure architecture decisions.
Collaborate across hardware, software, platform, and AI engineering teams to continuously improve system performance and reliability.

Requirements

What you’ll need

4+ years of experience in hardware engineering, systems engineering, or data center infrastructure.
Strong understanding of server architecture, CPUs, GPUs, memory, storage, networking, and hardware components.
Experience with AI infrastructure including NVIDIA GPUs, AMD accelerators, or other high-performance computing platforms.
Knowledge of PCIe, NVLink, InfiniBand, Ethernet, storage architectures, and high-speed interconnects.
Experience with Linux systems, hardware diagnostics, firmware updates, and system bring-up.
Familiarity with rack-scale deployments, data center operations, and hardware lifecycle management.
Understanding of thermal design, power management, and hardware reliability engineering.
Experience with infrastructure monitoring and hardware observability tools.
Knowledge of automation and scripting using Python, Bash, or similar languages is preferred.
Familiarity with Kubernetes, virtualization, or cloud infrastructure is a plus.
Experience supporting AI infrastructure for training or inference workloads is highly desirable.
AI Infrastructure: Experience deploying and optimizing infrastructure for large language models, deep learning, or distributed AI workloads.
Hardware Validation: Experience in hardware qualification, manufacturing validation, stress testing, and performance benchmarking.
Enterprise Infrastructure: Experience supporting enterprise data centers, on-premises deployments, or hybrid cloud infrastructure.
Platform Engineering: Exposure to infrastructure automation, provisioning systems, Infrastructure-as-Code, or hardware management platforms.

Benefits

Comp & perks

Founder-level ownership and bias for action.
Strong strategic thinking and ability to connect technical decisions to business impact.
Excellent communication and mentoring skills.
Thrives in ambiguity, fast-paced environments, and early-stage startup culture.
Work directly with high-pedigree founders shaping technical and product strategy.
Build infrastructure powering the future of AI compute globally.
Significant ownership and impact with equity reflective of your contributions.
Competitive compensation, flexible work options, and wellness benefits