Lead Engineer- AI QNX BSP engineer (CE)

Company: Qualcomm India Private Limited

Job Area: Engineering Group, Engineering Group > Software Engineering

General Summary: Role Summary We are seeking an experienced Agentic AI Lead Engineer to design and implement multi-agent AI systems for next-generation automotive platforms . This role will lead the architecture and development of agentic frameworks that enable autonomous reasoning, planning, and execution for in-vehicle experiences, ADAS, and connected car ecosystems . You will integrate LLM-based agents with automotive-grade software stacks, embedded platforms, and cloud services to deliver safe, reliable, and real-time AI capabilities.

Minimum Qualifications:

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.

OR

Master's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.

OR

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.

Exp: 6 to 9 yr

Key Responsibilities

• Architect Automotive Agentic Systems: Define agent roles (planner, executor, critic) for automotive workflows such as infotainment, navigation, diagnostics, and predictive maintenance.
• Tool Integration for Vehicle Ecosystem: Implement safe and scalable tool exposure for APIs like telematics, CAN bus, sensor fusion, and cloud services.
• Context & Memory Management: Build memory systems for persistent vehicle state, driver preferences, and contextual reasoning across sessions.
• Performance Optimization on Automotive Hardware: Enable on-device deployment on ECUs, SoCs, and Snapdragon Automotive Platforms; optimize inference for GPU/DSP accelerators under strict latency and power constraints.
• Safety & Compliance: Implement ISO 26262-aligned guardrails, policy enforcement, and observability for agent actions in safety-critical environments.
• Evaluation & Metrics: Develop automated evaluators for agent performance in automotive scenarios (e.g., route planning success, infotainment task completion).
• Cross-functional Leadership: Collaborate with platform, HMI, connectivity, and security teams; mentor engineers and drive technical direction.

Minimum Qualifications

• Bachelor’s degree in Engineering, Information Systems, Computer Science, or a related discipline.
• 5-7+ years in software engineering, with 3+ years in AI/LLM systems or orchestration.
• Strong programming skills in C/C++ and Python for embedded and cloud environments.
• Experience with ML frameworks (PyTorch, TensorFlow, ONNX) and model deployment on automotive-grade hardware.
• Hands-on experience integrating LLMs with automotive APIs, tools, and context management.
• Familiarity with agentic frameworks (LangChain Agents, AutoGen, DSPy)
• Proven leadership in delivering production software for automotive or embedded systems.
• Strong communication and leadership skills.

Preferred Qualifications

• Master’s degree in Science, Engineering, AI/ML or a relevant field
• Knowledge of vehicle data protocols (CAN, LIN, Ethernet) and telematics stacks.
• Experience with real-time constraints, functional safety, and compliance standards (ISO 26262, ASPICE).
• Knowledge on automotive middleware (Adaptive AUTOSAR, QNX, Linux).
• Understanding of connected car ecosystems, OTA updates, and edge-cloud orchestration.
• Quick learner with a robust technical background, strong problem-solving ability, and customer focus
• 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
• Systems architecture for agentic automotive pipelines.
• Embedded software optimization and resource management.
• Safety-first design and compliance awareness.

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