Senior GenAI Software Architect

• Build the hybrid AI collaboration layer connecting local AI and cloud AI.
• Design dynamic orchestration for planning, routing, and execution.
• Optimize AI workflows for latency, cost, privacy, and reliability.
• Enable agent-based systems with local planning and hybrid execution.
• Partner with product, systems, and runtime teams to ship production-ready platforms.
• Engage with industry-leading AI providers for co-engineering.
• Works with internal engineering teams and external partners, customers, and ISVs to deliver optimized AI solutions using Intel products with the goal of driving adoption of Intel products.
• Researches technical trends and utilizes deep domain and market segment expertise to prototype solutions and develop AI software to enable hybrid collaboration AI
• Optimizes performance of AI models through deep knowledge and expertise of AI frameworks, algorithms, models, and related hardware.
• Problem-solving skills with a results-driven, customer-focused mindset.
• Researches, develops, and adjusts new or existing AI models, code, parameters, and/or quantization to address issues and modify operations to enhance hybrid performance.
• Partners with AI algorithm and framework engineers as needed to optimize end-to-end AI models to Intel hardware features.
• Serves as a trusted technical advisor and provides technical enabling.
• Partners with Intel software and hardware product development teams to accelerate and optimize future products in AI and HPC domains by leading application reenabling and product hardening.
• Delivers competitive and differentiated benchmark collateral and identify and drive key workloads into product requirements and architect innovative projection methodologies and tools., The Client Computing Group (CCG) is responsible for driving business strategy and product development for Intel's PC products and platforms, spanning form factors such as notebooks, desktops, 2 in 1s, all in ones. Working with our partners across the industry, we intend to deliver purposeful computing experiences that unlock people's potential - allowing each person use our products to focus, create and connect in ways that matter most to them.

You must possess the below minimum qualifications to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates., * Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Data Science, Artificial Intelligence/Machine Learning, Information Technology or related STEM field.

• 8+ years hands-on experience on AI/ML algorithm development.
• 4+ years of experience in NLP, LLM-based systems, or AI agent development., * Experience in the following:
• GenAI algorithms, solution architecture, and performance tuning.
• experience building custom AI tools, agents, or apps for real-world use cases.
• Python or C++ skills.
• Client AI tools, cross-platform agents, or plugin ecosystems.
• Agent orchestration, task routing, or scheduling systems.
• RAG pipelines, vector databases (e.g.,FAISS, Chroma), and embedding techniques.
• GenAI workloads for edge devices using xPU accelerators.X
• LLMs (e.g., Mistral, Llama) or fine-tuning open-source models.
• Customer/partner support for GenAI workflow design and deployment.
• Frameworks such as LangChain, LlamaIndex, AutoGen, HuggingFace, and other APIs.
• UX/UI or prompt engineering to improve human-AI interaction.

We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation. Find out more about the benefits of working at Intel (https://intel.wd1.myworkdayjobs.com/External/page/1025c144664a100150b4b1665c750003) .

Annual Salary Range for jobs which could be performed in the US: $170,500.00-315,490.00 USD

Cloud-only AI is hitting real limits-cost, latency, privacy, and scale. We're building the next generation of Hybrid Collaboration AI systems, where local AI and cloud AI collaborate as one. This role is about defining how AI workloads are intelligently planned, routed, and executed across environments.