Edge Analytics Lead

Job Summary

The Edge Analytics Lead is responsible for designing, developing, and deploying advanced edge analytics capabilities on automotive Electronic Control Units (ECUs). The role focuses on real-time analytics, embedded AI/ML, edge computing, digital twin integration, cloud connectivity, predictive intelligence, and software-defined vehicle technologies to enable optimized vehicle performance and intelligent in-vehicle decision-making.

Key Responsibilities

• Design and implement edge analytics architectures for automotive ECUs.
• Develop distributed compute strategies across domain and zonal ECU architectures.
• Enable real-time processing of vehicle sensor data from CAN, Ethernet, ADAS sensors, and telematics systems.
• Develop and optimize analytics software for resource-constrained embedded platforms.
• Work across AUTOSAR Classic, AUTOSAR Adaptive, Linux-based ECUs, and High
• Performance Computing (HPC) platforms.
• Optimize CPU, GPU, and AI accelerator utilization for low-latency analytics and inference.
• Design and implement real-time in-vehicle data pipelines.
• Develop edge analytics use cases including predictive maintenance, driver behavior analytics, energy optimization (ICE & EV), safety analytics, and anomaly detection.
• Enable local decision-making with minimal dependency on cloud platforms.
• Design data filtering, aggregation, prioritization, and cloud offloading strategies.
• Integrate edge analytics outputs with enterprise big data platforms and cloud ecosystems.
• Support Digital Twin integration using real-time and historical vehicle data.
• Deploy and optimize AI/ML models on embedded automotive hardware using quantization, pruning, and edge optimization techniques.
• Manage AI model lifecycle including OTA (Over-the-Air) updates.
• Ensure compliance with automotive cybersecurity standards including AIS-189, AIS-190, and ISO 21434.
• Implement secure communication between vehicle ECUs and cloud platforms.
• Lead cross-functional teams of automotive engineers and data scientists while driving software-defined vehicle analytics strategy.

Required Skills & Experience

• 10+ years of experience in Automotive Embedded Systems or ECU Development.
• Minimum 3 years of experience leading engineering teams.
• 3+ years of experience in Edge Analytics, IoT, or In-Vehicle Data Systems.
• Strong expertise in ECU architectures including Domain Controllers, Zonal Architectures, and High-Performance Computing (HPC).
• Strong knowledge of Vehicle Networks including CAN, LIN, and Automotive Ethernet.
• Hands-on experience with AUTOSAR Classic, AUTOSAR Adaptive, Linux-based ECUs, and Real-Time Operating Systems (RTOS).
• Experience with Edge AI hardware platforms such as NVIDIA Jetson, Qualcomm, TI, and NXP.
• Exposure to Azure IoT, AWS IoT, and cloud data services.
• Experience integrating with Data Lakes, Kafka, Spark, or similar Big Data platforms.
• Strong programming skills in C, C++, and Python.
• Deep understanding of vehicle system architecture and embedded optimization.
• Experience bridging edge intelligence with cloud analytics platforms.
• Strong leadership, architecture, and strategic planning capabilities.

Preferred Qualifications

• Bachelor’s or Master’s degree in Electronics, Computer Science, Automotive Engineering, or related field.
• Experience in Commercial Vehicles, Fleet Analytics, or Telematics systems.
• Knowledge of EV and ICE powertrain analytics.
• Experience with Digital Twin platforms such as Azure Digital Twins or Siemens Digital Twin.
• Familiarity with embedded analytics frameworks and edge processing technologies.

Other Requirements

• Strong leadership and team management capabilities.
• Excellent communication and stakeholder management skills.
• Ability to work across embedded systems, cloud platforms, and automotive engineering teams.
• Strategic mindset with a focus on Software-Defined Vehicle (SDV) innovation and future mobility technologies.