Performance Engineering Architect
Hewlett Packard Enterprise (HPE) (company site, opens in a new tab)BengaluruOn-siteDirect career pageEst. 35–55 LPA
Posted on 22 Sept
Reach people at Hewlett Packard Enterprise (HPE)
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Hewlett Packard Enterprise (HPE) you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead performance characterization and optimization of AI solutions across GPUs, servers, storage, networking, and system software.
- Design and execute benchmarking studies for generative AI, ML workloads; produce performance reports and recommendations supporting product design and customer decisions.
- Develop and automate benchmarks, analyze telemetry, and collaborate cross-functionally with engineering, product, sizing, and partner teams.
Minimum Requirements
- Strong expertise in generative AI and ML performance, with hands-on experience benchmarking AI workloads on GPU-accelerated systems.
- Proficiency in Linux administration, Python and shell scripting for automation, and working knowledge of AI performance tools and telemetry platforms.
- Deep understanding of server hardware architecture (CPU, GPU, NUMA, PCIe, storage, networking) and experience configuring/troubleshooting enterprise servers.
- Work Experience Required: Not explicitly mentioned in the JD; Role requires onsite work from an HPE office.
Ideal Candidate Profile
- Experienced in detailed system-level performance analysis spanning hardware and software stack to identify bottlenecks and optimize AI workloads.
- Skilled in establishing scalable, repeatable performance test methodologies and automating measurement pipelines for AI inference and training.
- Able to communicate complex performance insights to diverse stakeholders and collaborate effectively across engineering, product, and field teams.