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About This Role
- AI Software Solution Engineer for validation and workload enabling role to work with internal engineering teams and ecosystem partners to deliver high-performance AI solutions optimized for Intel platforms.
- Explores emerging AI trends, prototypes advanced solutions, and drives adoption of Intel’s AI capabilities across cloud, edge, and client markets.
- Enhances AI model efficiency, accuracy, and performance through deep understanding of frameworks, algorithms, and underlying hardware.
- Enable AI models on Intel GPUs for accuracy and optimize for performance.
- Acts as a trusted technical leader supporting product enablement, performance tuning, validation, and benchmarking to help shape future Intel AI architectures and platforms.
- Key Responsibilities: Collaborate with cross-functional hardware and software engineering teams to validate AI workloads for Intel architectures.
- Evaluate and debug deep-learning models, kernels, and operators to maximize performance and efficiency while maintaining accuracy.
- Conduct benchmarking, regression analysis, and algorithmic validation across a variety of use-cases and frameworks.
- Develop prototype workloads, tools, and automation pipelines to accelerate performance tuning and validation workflows.
- Conduct performance and accuracy evaluation of AI models on competition HW to detect and plug the gaps.
- Engage with customers, ISVs, and internal development groups to drive enablement, performance improvements, and ecosystem readiness.
- Translate AI workload needs actionable architecture and product insights and support next-generation platform bring-up, pre-silicon modeling, and product maturity efforts.
Qualifications
- Bachelor’s/master’s in computer science, Electronics Engineering, Mathematics, or related field with 8–15 years of experience.
- Knowledge of ML/DL is a Must including LLM Architecture, Transformer, Attention, Low precision Data type (fp8/fp4), Quantization Techniques, Open source upstreaming, Inference Serving etc.
- AI Workload enabling including accuracy debug and performance optimization is must.
- Hands-on experience with ML/DL models and distributed training/inference using PyTorch, Tensorflow, vLLM/SGLang or similar frameworks.
- Strong skills in performance debugging, numerical analysis, and regression tracking in validation environments.
- Working knowledge of Agentic AI deployment is a plus.
- Strong skills in validation framework design and test case development for High level frameworks like SGLang/vLLM Proficiency in Python (NumPy, SciPy, Pandas, PyTest) Solid Linux development/debugging experience (git, cmake, gdb, strace, perf), and familiarity with Git/GitHub/Gerrit workflows and CI/CD automation.
- Understanding of distributed systems, HPC/GPU scaling, MPI/torchrun/Fully Sharded Data Parallel/Tensor Parallel, and high-performance networking (Ethernet/InfiniBand).
- Skilled in Docker/Kubernetes, virtualization, performance benchmarking, and automation.
- Strong analytical, problem-solving, and communication skills with ability to work across architecture, development, and validation teams.
Sourced directly from Intel’s career page
Your application goes straight to Intel.
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Specialisation
Open roles at Intel
98 positions
Job ID
/job/India-Bangalore/AI-Validation--Workload-Enabling-and-Tools-Engineer_JR0283574
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