AI ML Lead Engineer- CE Team

Location: Bangalore, Karnataka, India

Department: Software Engineering

Work Location: onsite

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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