2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.
Execution & Delivery
Oversee end‑to‑end delivery of AI systems from research to production, including model onboarding, optimization, validation, and lifecycle management.
Ensure performance, power, latency, memory, and accuracy targets are met across heterogeneous compute (CPU/GPU/DSP/NPU).
Establish best practices for model optimization (quantization, compression, batching, KV‑cache tuning, mixed precision).
Team & Organizational Leadership
Build, mentor, and scale multi‑disciplinary AI/ML engineering teams (ML engineers, systems engineers, platform engineers).
Set technical direction, define career growth paths, and uphold a high bar for engineering excellence.
Foster a culture of ownership, accountability, and innovation.
Minimum Qualifications
Bachelor’s degree in Engineering, Computer Science, or a related field.
25+ years of industry experience in software engineering.
20+ years of people management experience, including managing managers or senior technical leads and customers.
Strong background in AI/ML system design, not just model development.
Hands‑on experience with ML frameworks (PyTorch, TensorFlow, ONNX) and edge/embedded deployment.
Proven ability to deliver large‑scale, production AI systems in complex environments
Preferred Qualifications
Master’s or PhD in AI/ML, Computer Science, or related field.
Deep understanding of LLMs, VLMs/LVMs, multimodal and agentic AI architectures.
Experience with automotive or industrial platforms, embedded Linux/QNX, and real‑time constraints.
Familiarity with QNN‑like runtimes, inference SDKs, and heterogeneous acceleration.
Strong customer‑facing experience, including executive‑level technical discussions.
Track record of influencing product and platform roadmaps through technical leadership.
Experience in software architecture and design patterns
Knowledge of architectural approaches for large-scale software applications
Previous customer-facing experience is advantageous
Exposure to fine-tuning GenAI models and reinforcement learning considered a plus
