Principal Generative AI Solutions Architect

Qualcomm seeks a Principal, Gen AI Transformation leader to drive large scale AI initiatives across its Telecommunications & Media portfolio. This role defines the GenAI strategy, partners with product, engineering, and business units, and converts use cases into scalable solutions spanning devices, networks, and cloud. You will lead discovery workshops, design reference architectures, and guide pilots to production, ensuring security, compliance, and performance. Collaborate with global teams to embed AI into workflows, measure business impact, and build an enterprise roadmap that accelerates innovation and competitive advantage.

Responsibilities

• Define and drive Qualcomm's enterprise Gen
• AI transformation strategy across products and business units
• Identify, prioritize, and shape high value Gen
• AI use cases with telecommunications and media stakeholders
• Design scalable Gen
• AI architectures spanning device, edge, network, and cloud platforms
• Lead end to end delivery of Gen
• AI pilots and scale them to secure, compliant production solutions
• Collaborate with engineering, product, and operations to embed AI into tools, workflows, and services
• Establish governance, risk, and performance metrics for Gen
• AI solutions and model lifecycle
• Partner with data, security, and IT teams to ensure robust data pipelines and responsible AI practices
• Evangelize Gen
• AI capabilities, run workshops, and upskill internal teams on best practices
• Measure and communicate business impact, creating executive ready roadmaps and progress updates
• Stay current on AI/ML, LLM, and telecom innovations to inform Qualcomm's technology strategy

Required Skills

• Gen
• AI and large language models (LLMs)
• Machine learning and deep learning
• MLOps and model lifecycle management
• Cloud platforms (AWS, Azure, or GCP)
• Data engineering and pipelines
• Architecture design for scalable AI systems
• Python or similar programming languages
• API integration and microservices
• Model evaluation, monitoring, and observability
• Security, compliance, and responsible AI practices