AI ML Lead Engineer- CE Team
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 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. 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. 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.
