AI Software Validation Engineer

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About the role

Job Details: Job Description: The Role and Impact As an AI Frameworks Engineer in the Conversion, Performance, and Accuracy team, you will play a critical role in enabling the latest GenAI and Deep Learning models within the OpenVINO™ ecosystem. Your day-to-day focus will be Model Enabling Support. You will be a key member of the team responsible for building the performance and accuracy measurement frameworks, automation pipelines, and testing ecosystems that validate new AI models. By delivering reliable test environments and clear validation results, you will directly support and accelerate the integration of the newest AI models into OpenVINO™ and Optimum-Intel, helping us scale support for a wider scope of cutting-edge AI architectures.   Business Group The Software Group at Intel is driven by innovation, focusing on developing world-class tools and platforms that empower developers globally. As part of this team, you will contribute directly to Intel's broad mission of delivering robust, scalable, and high-performance AI inference on Intel hardware (CPUs, GPUs, and NPUs) by ensuring the OpenVINO ecosystem remains compatible with the newest state-of-the-art models. Key Responsibilities • Model Enabling & Integration: Enable and validate new AI models in OpenVINO, Optimum-Intel, and internal performance and accuracy tools. • Benchmarking & Evaluation: Evaluate quantized and native models on Intel hardware, collecting critical metrics for performance, memory consumption, and accuracy. • Tool Development: Actively develop and extend opensource benchmarking and validation tools to unblock validation capabilities for new models. • Automation & CI/CD Pipelines: Build and maintain dedicated daily pipelines for model enabling, using Jenkins (utilizing Groovy and Python scripting). • Automated Log Analysis: Design and implement automated solutions to parse execution logs and pinpoint required changes or additions for successful model enablement. • Process & Workload Optimization (Continuous Improvement): Continuously identify bottlenecks in validation workloads, test execution cycles, and infrastructure; drive initiatives to streamline team workflows and operational efficiency. • Transparent Reporting & Communication: Deliver clear, actionable status reports, dashboards, and metrics on validation progress, blockers, and model support readiness to team members and project stakeholders. Qualifications: Minimum Qualifications • Education: Bachelor's degree in Computer Science, Computer Engineering, or a related field with 5–6 years of experience. • Scripting & Automation: Strong proficiency in scripting languages—primarily Python—and familiarity with Groovy or Bash. • Problem-Solving & Log Parsing: Strong ability to build regex, parse complex logs, and develop automated scripts to streamline triage and identify execution failures. • CI/CD Systems: Hands-on experience building or maintaining CI/CD pipelines in Jenkins or similar tools. • Development Environment: Strong experience with Linux, Git, and Python testing frameworks (e.g., PyTest). • Model Exploration & Curiosity: Passion for playing with, testing, and adopting new AI models as they are released, with a proven track record of using them in previous work assignments, personal hobby projects, or open-source contributions. Preferred Qualifications • Inference Toolkits (Great Asset): Familiarity with OpenVINO™, Optimum-Intel, or other deep learning inference runtimes (e.g., ONNX Runtime, TensorRT, llama.cpp). • Performance & Accuracy Profiling: Experience using benchmarking tools or accuracy check frameworks. • Model Quantization: Basic understanding of model quantization/compression concepts and evaluating native vs. quantized models. What we offer: At Inte

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