Design, implement, and optimize real-time embedded modem firmware across protocol layers and drivers while meeting strict latency, memory, and power constraints. Pioneer AI-native development practices by integrating AI coding agents, LLM-based code review, and automated test synthesis into daily workflows to accelerate delivery and debugging. Own end-to-end feature delivery from requirements through on-target bring-up, and debug complex multi-layer hardware issues using AI-assisted analysis. Requires deep expertise in modern C/C++, embedded systems (DSP/Arm architectures), RTOS internals, data structures, and hands-on target hardware debugging, combined with practical experience using AI coding assistants and agentic tools.

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.