Senior AI Software Engineer responsible for designing, developing, and deploying LLM-powered applications and AI-assisted development workflows within a large-scale enterprise Linux environment (~6M+ LOC, primarily C/C++). Build high-performance GPU-based inference pipelines using vLLM and modern frameworks, develop agentic AI workflows with RAG and tool calling, and integrate LLMs with vector databases and enterprise systems into production.… Collaborate across software engineers, AI researchers, and platform teams to productionize AI services while optimizing for performance, latency, scalability, and operational efficiency. Requires 4+ years software development, strong Linux/Red Hat experience, advanced C/C++, Python, LLM/RAG expertise, and GitLab CI/CD workflows.
tool-calling jobs in San Diego
Lead Qualcomm's GenAI transformation for embedded and software engineering, building reusable AI agents, agentic workflows, and developer productivity tools that scale across teams. Partner with platform and engineering teams to establish secure AI practices, governance models, and metrics while serving as a technical thought leader who shapes the organization's transition to AI-native engineering workflows.… Requires Principal-level technical leadership with deep expertise in generative AI, LLM-based systems, and hands-on experience deploying GenAI solutions in complex engineering environments. Collaborate on code generation, code review, test automation, and release readiness use cases while driving measurable productivity and quality improvements.
Design, build, and ship LLM-powered capabilities end to end—from prototyping and fine-tuning models to deploying production agents and retrieval systems. Own the full stack: prompt and context engineering, multi-step agent design with tool calling, RAG systems (embeddings, chunking, hybrid search, reranking), fine-tuning on multi-GPU with LoRA/QLoRA, evaluation and tracing infrastructure, and clean APIs for other engineers.… Work in a secure, distributed environment where the platform runs on customer compute, cloud, or hybrid setups, requiring expertise with both commercial and self-hosted models.
Build the foundational agentic AI layer for a materials-science platform, including multi-model provider abstraction, agent orchestration with stateful checkpoints, retrieval systems, prompt versioning, and comprehensive tracing and evaluation frameworks. You'll design agents that plan and reason over tool calls in production, implement human-in-the-loop safety gates, and ensure all LLM behavior remains auditable and cost-tracked across customers' secure environments.… The role demands deep production experience with agentic and LLM systems: async Python, structured outputs, memory and context management, multi-step workflow orchestration, and evaluation harnesses that catch regressions before deployment.