Sr. Engineer, AI Platforms and Solutions

Qualcomm•San Diego, CA

5d•$111,300 - $166,900•Onsite

About The Position

We are looking for a hands-on Senior Software Engineer for our Qualcomm’s Enterprise AI Platform and Solutions team. In this role, the candidate will contribute to the design, development, and delivery of next-generation AI-powered applications, developer experiences, and reusable platform capabilities across the enterprise. This is a fast-paced, execution-driven position that requires strong ownership, the ability to deliver under tight timelines, and the ability to deliver production-quality solutions under evolving requirements. This role requires full-time onsite work in San Diego, CA (5 days per week).

Requirements

Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.
PhD in Engineering, Information Systems, Computer Science, or related field.
2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.
Strong programming skills in Python, Java, C#.NET, Rust, JavaScript, TypeScript, React.js, Angular, Node.js, HTML, and CSS
Experience designing and building full-stack applications, including user interfaces, backend services, APIs, data integration layers, and distributed systems.
Hands-on experience developing, integrating, or deploying AI-powered applications utilizing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), semantic search, or agentic workflows.
Experience building enterprise search, retrieval, indexing, or knowledge management solutions using modern search technologies, vector databases, embeddings, or related capabilities.
Experience developing and deploying cloud-native applications on AWS, Azure, or Google Cloud Platform, with familiarity in containerized and Kubernetes-based environments.
Strong understanding of API design, microservices architectures, application integration patterns, authentication, authorization, and frontend/backend interaction.
Experience debugging across both traditional systems and AI/LLM-driven behavior

Nice To Haves

Tracing/observability tools for LLM systems
Experience with Elasticsearch / vector search
Docker / Kubernetes exposure
Multi-agent systems and evaluation frameworks

Responsibilities

Build and optimize Retrieval-Augmented Generation (RAG) solutions, including document ingestion, indexing, embeddings, vector search, reranking, retrieval optimization, grounding, and evaluation frameworks.
Develop and integrate LLM-powered capabilities, agents, and AI services into enterprise applications to improve knowledge discovery, automation, productivity, and business workflows.
Design, develop, and enhance end-to-end AI-powered enterprise applications, including backend services, APIs, agentic workflows, and modern user experiences.
Architect, deploy, and operate scalable cloud-native AI solutions on Kubernetes and public cloud platforms, ensuring high availability, security, reliability, and performance at enterprise scale.
Design and implement APIs, MCP-compatible services, enterprise connectors, and integration layers that expose AI, search, and platform capabilities across applications.
Build and maintain production-ready CI/CD pipelines, infrastructure automation, observability, monitoring, tracing, and operational tooling for AI applications and platform services.
Optimize system performance across application, retrieval, inference, and data-processing layers, including latency, throughput, scalability, resiliency, and cost efficiency.
Collaborate with AI/ML, platform, infrastructure, security, and product teams to deliver robust, production-grade AI solutions while establishing engineering best practices, governance, and operational excellence.