Director Engineering - AI software stack

Qualcomm · Bengaluru · 3+ yrs experience · Posted 2026-02-17

Tech stack: C, C++, Java, Linux, Python

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About the role

Qualcomm India Private Limited
Engineering Group, Engineering Group > Software Engineering
Responsibilities: - As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all.
- As a Qualcomm Software Engineer, you will
- design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized utility programs that launch cutting-edge, world class products that meet and exceed customer needs.
- Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces.
- AI/ML Strategy & Platform Leadership
- Define and own the AI/ML technology roadmap aligned with business goals across automotive and edge AI platforms.
- Drive adoption of LLMs, VLMs/LVMs, multimodal AI, and agentic AI systems for real‑world, safety‑critical deployments.
- Influence platform and silicon‑level decisions by translating AI workload requirements into hardware and software capabilities.
Qualifications: - Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
- PhD in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.
- 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.
- 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
- 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
- Expertise in inference accuracy, throughput optimization, and edge deployment highly desirable
- Lead system-level problem triage to identify root causes and communicate testing/debugging results to relevant stakeholders

Qualifications

- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
- PhD in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.
- 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.
- 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
- 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
- Expertise in inference accuracy, throughput optimization, and edge deployment highly desirable
- Lead system-level problem triage to identify root causes and communicate testing/debugging results to relevant stakeholders

Responsibilities

- As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all.
- As a Qualcomm Software Engineer, you will
- design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized utility programs that launch cutting-edge, world class products that meet and exceed customer needs.
- Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces.
- AI/ML Strategy & Platform Leadership
- Define and own the AI/ML technology roadmap aligned with business goals across automotive and edge AI platforms.
- Drive adoption of LLMs, VLMs/LVMs, multimodal AI, and agentic AI systems for real‑world, safety‑critical deployments.
- Influence platform and silicon‑level decisions by translating AI workload requirements into hardware and software capabilities.

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