AI Benchmarking & Performance Engineer

L&T Technology Services; Bengaluru; On-site; INR 4,200,000 - 5,400,000; Full time

Job summary

L&T Technology Services is seeking an AI Benchmarking & Performance Engineer in Bengaluru to evaluate AI workloads on CPU, GPU, and NPU architectures. You will design repeatable benchmarks, analyze latency, throughput, and resource use, and optimize AI inference pipelines for robotics, edge AI, and production deployments.

The role collaborates with software, hardware, and AI teams to deliver actionable insights and drive scalable performance improvements across platforms and solutions.

Qualifications

• 8-12 years of experience in Performance Engineering, System Analysis, or AI Platform Optimization.

Responsibilities

• Benchmark AI models such as OpenVLA, YOLOv8, Whisper, Pi Droid, Groot, MLPerf Client.

Skills

Performance engineering

System analysis

AI platform optimization

Linux performance

Education

Bachelors / Masters in CS/EE/AI/Robotics

Tools

PyTorch

ONNX Runtime

TensorRT

OpenVINO

perf

Intel VTune

NVIDIA Nsight

MLPerf

Docker

ROS/ROS2

Job description

Job Description – AI Benchmarking & Performance Engineer

Job Title: AI Benchmarking & Performance Engineer

Experience: 8-12 Years

Education: Bachelor's or Master's Degree in Computer Science, Electronics, Electrical Engineering, AI/ML, Robotics, or related field

Role Overview

We are seeking a highly skilled AI Benchmarking & Performance Engineer to evaluate, analyze, and optimize AI workloads across modern computing platforms including CPU, GPU, and NPU architectures. The ideal candidate will have strong expertise in AI model benchmarking, system-level performance analysis, profiling, and optimization for real-world AI applications spanning computer vision, speech processing, robotics, and edge AI deployments.

This role requires close collaboration with software, hardware, and AI engineering teams to deliver actionable performance insights and drive optimal utilization of computing resources.

Key Responsibilities

• Benchmark AI models such as OpenVLA, YOLOv8, Whisper, Pi Droid, Groot, MLPerf Client, and similar AI workloads.
• Analyze key performance metrics including:Latency
• Throughput (FPS)
• Resource Consumption
• Design and execute repeatable benchmark pipelines across multiple hardware platforms.
• Perform system-level performance analysis to identify bottlenecks in:CPU
• GPU
• NPU
• Memory Subsystems
• Optimize AI inference pipelines through profiling and performance tuning techniques.
• Integrate AI models into robotics, edge AI, and production application workflows and evaluate end-to-end performance.
• Conduct competitive performance comparisons across platforms such as AMD, NVIDIA, Qualcomm, Intel, and ARM-based architectures.
• Develop automation frameworks for benchmark execution, result collection, and reporting.
• Generate technical reports, comparative analysis, and performance recommendations for engineering and business stakeholders.
• Collaborate with cross-functional teams to improve AI deployment efficiency and scalability.

Required Skills

• 8-12 years of experience in Performance Engineering, System Analysis, or AI Platform Optimization.
• Strong understanding of AI/ML model architectures and inference workflows.
• Hands-on experience with:PyTorch
• ONNX Runtime
• TensorRT (Preferred)
• OpenVINO (Preferred)
• Strong experience in Linux environments and performance analysis tools.
• Knowledge of modern:CPU architectures
• NPU/AI Accelerators
• Memory hierarchy and system architecture
• Experience with profiling and benchmarking tools:perf
• Intel VTune
• NVIDIA Nsight
• ROCm profiling tools
• MLPerf benchmarking frameworks
• Strong programming skills in:Python
• C/C++
• Shell/Perl scripting
• Excellent debugging, analytical, and problem-solving capabilities.
• Experience working with large-scale performance datasets and generating performance insights.

Preferred Skills

• Experience with Robotics frameworks such as ROS/ROS2.
• Knowledge of Edge AI deployment and optimization.
• Experience with simulation environments and validation platforms.
• Familiarity with container technologies:Docker
• Experience optimizing AI workloads on embedded platforms.
• Understanding of model quantization, pruning, and acceleration techniques.
• Exposure to autonomous systems, robotics, or computer vision applications.