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AI/ML Validation Engineer - Data Modeling Job Opening In Bengaluru – Now Hiring Semi leaf


Job description

<p><p>- Experience : 3 to 6 Years<br/><br/></p><p>- Notice Period : Immediate or Less than 1 Month<br/><br/></p><p>- Location : Bangalore</p><br/><p><b>Key Responsibilities : </b><br/><br/>- <b>Model conversion pipelines (e.g., PyTorch ONNX)</b><br/><br/>- Inference runtimes : ONNX Runtime, TensorRT, ROCm/HIP<br/><br/>- Toolchains and compilers like TVM, XLA, Vitis AI, XDNA<br/><br/>- <b>Kernel execution, memory transfer, and result validation</b><br/><br/>- Profile ML workloads and optimize for performance.<br/><br/>- Work on validation plans across software lifecycle stages.<br/><br/>- Collaborate with cross-functional and global teams on end-to-end AI/ML pipeline validation.</p><br/><p><b>Required Skills : </b><br/><br/>- <b>Strong hands-on experience with Linux (Ubuntu) and Yocto.</b><br/><br/>- Solid understanding of machine learning concepts : deep learning, LLMs, recommender systems.<br/><br/>- Proficiency with Python programming.<br/><br/>- <b>Experience in ML frameworks : PyTorch, TensorFlow, ONNX Runtime.</b><br/><br/>- <b>Experience profiling AI/ML workloads.</b><br/><br/>- Prior exposure to GPU/Accelerator validation stacks (HIP, CUDA, OpenCL, OpenVINO).<br/><br/>- Familiarity with production-level QA methodologies and defect lifecycle management.<br/><br/>- Excellent analytical and problem-solving skills.<br/><br/>- Strong communication skills and sense of ownership.</p><br/></p> (ref:hirist.tech)

Required Skill Profession

Computer Occupations


  • Job Details

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The Work Culture

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The average salary range for a varies, but the pay scale is rated "Standard" in Bengaluru. Salary levels may vary depending on your industry, experience, and skills. It's essential to research and negotiate effectively. We advise reading the full job specification before proceeding with the application to understand the salary package.

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Semi leaf interview tips for AI/ML Validation Engineer   Data Modeling

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Answer Thoughtfully: Listen carefully, take a moment to formulate clear and concise responses. Highlight relevant skills and experiences using the STAR method.

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