AI Software Solutions Engineer
- Compensation
- Not disclosed
- Location
- India, Bangalore
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Solutions engineering
- Posted
Skills
Must have
- Bachelors with 8+, Masters with 6+ years, or PhD with 3+ years, of relevant industry experience in Computer Science or Computer Engineering or Electrical Engineering related technical discipline.
- Programming in Python/C++, strong in Data Structures/Algorithms, worked on large SW projects.
- Knowledge of computer architecture and compiler fundamentals.
- Strong written and verbal communication skills.
Nice to have
- Familiarity with AI algorithms (e.g. LLMs) and DL and serving frameworks (e.g. PyTorch, JAX, vLLM, SGLang, HF).
- Advanced understanding of machine learning concepts and deep learning practices.
- Knowledge of Intel hardware architecture and features, including CPUs, GPUs, or AI accelerators.
- Demonstrated ability to analyze, prototype, and solve complex technical challenges.
- Strong communication skills to convey technical concepts to diverse audiences.
- Experience with developing and integrating CUTLASS or Triton-based kernels in large language models (LLMs).
- Knowledge of compiler algorithms for heterogeneous systems and fuser optimizations.
- Effective communication skills and experience working in cross-geo teams.
- Familiarity with generative AI models and LLMs.
Full description
Job Details:
Job Description:
The Role and Impact:
As an AI Software Solutions Engineer, you will focus on optimizing AI solutions using Intel products, working closely with open-source frameworks like PyTorch, TensorFlow, and JAX. Your day-to-day responsibilities will involve designing and developing features for Intel's AI frameworks software stack, enabling and optimizing deep learning models, and enhancing AI frameworks for Intel's cutting-edge GPUs and accelerators. This role is pivotal to driving advancements in AI technology, ensuring Intel products deliver exceptional performance for AI workloads.
Business Group:
The role resides within Intel's Data Center Group (DCG), a business unit dedicated to advancing data center and AI technologies that power major innovations across industries. DCG supports Intel's broader mission by developing solutions that address today's challenges in AI, HPC (High-Performance Computing), and enterprise workloads. The team collaborates globally to deliver impactful technologies that shape the future of computing.
Key Responsibilities:
- Propose optimizations in SW and kernel implementation to improve workload performance.
- Model AI HW and SW (GPU/accelerator) for performance analysis. Analyze AI workloads to identify critical kernels and patterns. Estimate ideal performance of kernels and workloads for a given HW spec.
- Enhance and maintain the performance modeling toolkit for projections. Correlate projected performance with device/simulator and improve models.
- Study new and different DL networks for future optimization requirements.
- Design and develop hardware-agnostic and hardware-aware software features for AI frameworks, focusing on ML kernel development.
- Enhance and extend deep learning training and inference capabilities within Intel's AI software stack.
- Debug and resolve functional and convergence issues in multi-layered software systems.
Qualifications:Minimum Qualifications:
- Bachelors with 8+, Masters with 6+ years, or PhD with 3+ years, of relevant industry experience in Computer Science or Computer Engineering or Electrical Engineering related technical discipline.
- Programming in Python/C++, strong in Data Structures/Algorithms, worked on large SW projects.
- Knowledge of computer architecture and compiler fundamentals.
- Strong written and verbal communication skills.
- Expertise in performance optimization techniques for AI workloads on modern hardware architectures.
Preferred Qualifications :
- Familiarity with AI algorithms (e.g. LLMs) and DL and serving frameworks (e.g. PyTorch, JAX, vLLM, SGLang, HF).
- Advanced understanding of machine learning concepts and deep learning practices.