Director Engineering - AI software stack

Lead Qualcomm’s AI software-stack strategy and delivery for automotive and edge AI platforms in Bangalore, India. The role owns the AI/ML technology roadmap, advances LLM, vision-language, multimodal and agentic AI systems for safety-critical deployments, and translates AI workload needs into platform, silicon, hardware and software decisions.

Responsibilities

• Oversee AI systems from research through production, including model onboarding, optimization, validation and lifecycle management. Meet performance, power, latency, memory and accuracy targets across CPU, GPU, DSP and NPU compute. Establish practices for quantization, compression, batching, KV-cache tuning and mixed precision.
• Build, mentor and scale multidisciplinary ML, systems and platform engineering teams. Set technical direction and career paths, and foster engineering excellence, ownership and accountability.
• Collaborate across systems, hardware, architecture and testing teams on system-level software solutions. Lead problem triage, identify root causes and communicate testing and debugging results to stakeholders.

Required qualifications

• Bachelor’s degree in Engineering, Computer Science or a related field; 25+ years of software engineering industry experience and 20+ years of people management experience, including managing managers or senior technical leads and customers.
• Strong AI/ML system-design background, hands-on PyTorch, TensorFlow and ONNX experience, edge or embedded deployment experience, and a record of delivering large-scale production AI systems.
• The posting also lists alternative general minimums: a related bachelor’s degree with 3+ years of software engineering or related experience, a master’s with 2+ years, or a PhD with 1+ year; plus 2+ years of academic or work experience with a programming language such as C, C++, Java or Python.

Preferred qualifications

• Master’s or PhD in AI/ML, Computer Science or a related field; understanding of LLM, vision-language, multimodal and agentic architectures; automotive or industrial platforms, embedded Linux or QNX, and real-time constraints.
• Familiarity with QNN-like runtimes, inference SDKs and heterogeneous acceleration; customer-facing and executive-level technical communication; and experience influencing product roadmaps and designing large-scale software architectures. GenAI fine-tuning and reinforcement learning are pluses; inference accuracy, throughput optimization and edge-deployment expertise are highly desirable.