AI Validation Engineer embedded systems:

Key Responsibilities

AI/ML Validation & Quality Ownership on Ubuntu & Yocto OS Variants

- Lead the Team & Exeuctie validation efforts for complex AI/ML compute stacks across multiple platforms and environments
- Define, develop, and execute comprehensive validation plans, test strategies, and methodologies for AI software and system pipelines
- Own the defect lifecycle, including reporting, triage, root cause analysis, and closure
- Ensure validation coverage across functional, performance, stress, regression, and scalability testing

End to End AI Pipeline Validation
- Validate end to end AI workflows, including:

Validate end to end ROCmLibraries o Model training, conversion, and optimization (e.g., PyTorch → ONNX)

o Inference runtimes (ONNX Runtime, TensorRT, ROCm/HIP, OpenVINO)

o Compilers and toolchains (TVM, Vitis AI, XDNA, XLA)

o Kernel execution, memory transfers, and inference accuracy

- Validate correctness, stability, and performance of AI workloads on Linux based environments (Ubuntu, Yocto)

AI Benchmarking, Profiling & Optimization
- Define and execute AI benchmarking strategies for training and inference workloads across hardware platforms
- Perform performance profiling and analysis of AI models to identify compute, memory, and latency bottlenecks
- Collaborate with compiler, runtime, and hardware teams to drive model level and system level optimizations
- Validate performance improvements across model architectures, batch sizes, precision modes (FP32, FP16, INT8), and execution paths
- Ensure performance regressions are detected early and performance KPIs are met for production releases

ML Frameworks & Compute Stack Expertise
- Validate integration, functionality,



and performance of AI frameworks including PyTorch, TensorFlow, and ONNX Runtime
- Execute and validate AI/ML workloads on compute stacks such as ROCm/HIP, CUDA, OpenCL, and heterogeneous accelerators
- Analyze performance impact of framework, compiler, and runtime changes on real world AI models

Automation & Tooling
- Design and implement Python based validation and benchmarking frameworks
- Develop reusable automation for test execution, benchmarking, profiling, result analysis, and reporting
- Drive continuous improvements in validation efficiency, benchmarking scalability, and coverage

Technical Leadership & Collaboration
- Work closely with architecture, compiler, runtime, driver, and hardware teams to debug performance and functional issues
- Provide technical guidance and mentorship to junior engineers and validation teams
- Collaborate effectively with global, cross functional teams across time zones
- Communicate validation, benchmarking results, performance risks, and quality metrics clearly to stakeholders

Required Skills & Qualifications:

8 to 12 years Experience in AI Validation & Strong Python & Scripting knowledge

Technical Expertise

- Strong background in machine learning fundamentals, including:

o Deep learning o Large Language Models (LLMs)

o Recommender systems

- Strong experience with ROCm concepts and ROCm validation




- Hands on experience with AI benchmarking, profiling, and performance optimization of AI models
- Prior experience validating AI/ML compute stacks, including:

o HIP, CUDA, OpenCL o OpenVINO o TensorFlow / PyTorch integrations
- Experience validating end to end AI pipelines, including model conversion, inference runtimes, compilers, kernel execution, and accuracy
- Strong Python programming skills for automation, benchmarking, and validation
- Experience profiling AI workloads to optimize throughput, latency, and resource utilization
- Strong knowledge of Linux environments, specifically Ubuntu and Yocto

Validation, Quality & Process
- Strong experience with software validation processes, defect management, and SDLC
- Experience with production level quality assurance practices and performance driven validation
- Strong focus on test quality, performance coverage, reproducibility, and release readiness
- Ability to work independently with solid ownership of deliverables

Soft Skills

- Excellent problem solving abilities with a performance driven and analytical mindset
- Strong verbal and written communication skills
- Proven ability to work effectively with global and cross functional teams

Preferred Qualifications

- Experience benchmarking and optimizing AI workloads on heterogeneous hardware platforms (CPU, GPU, AI accelerators)
- Exposure to performance tuning of large scale models, including memory optimization and mixed precision execution
- Experience contributing to or working with open source AI and performance focused projects

Education

Bachelor’s or Master’s degree in AI/ML, Robotics, Electronics, Computer Science, or a related field.

📌 AI Validation Engineer embedded systems (Bengaluru)
🏢 Lu0026T Technology Services
📍 Bengaluru