Systems Engineer

Responsibilities Partner with cross‑functional teams to define "what" a vehicle needs to observe. You will write the technical requirements that govern how ECUs log data and how the Cloud interprets it. Bridge the gap between Embedded C++ firmware and Cloud‑based Python microservices. You will ensure that the "handshake" between the vehicle and the AI reasoning engine is seamless and scalable. Help mature the intelligent diagnostic workflows, ensuring the AI has the right "context" (DTCs, PIDs, and logs) to perform automated root‑cause analysis. Lead the system integration testing, simulating complex failures to ensure our End‑to‑End (E2E) pipeline triggers the correct alerts and human‑support processes. Analyze real‑world telemetry to refine requirements and iterate on the next generation of diagnostic capabilities. Define the specific telemetry hooks (logs, metrics, and traces) required from embedded ECUs to power cloud‑based AI reasoning. Build and validate the "Diagnostic Loop"—the path from a vehicle fault code (DTC) to an AI‑generated repair recommendation. Define the API contracts between the vehicle's embedded gateway and the cloud‑based diagnostic orchestrator. Quantify the accuracy of AI diagnostic models by designing and running validation tests against known vehicle "ground truth" data. Engineer AI‑powered diagnostic capabilities that combine vehicle signals (DTCs, PIDs, Ethernet logs) with LLM‑based reasoning to automate root‑cause isolation. Define the requirements for "Vehicle Telemetry 2.0"—determining exactly what traces, metrics, and logs are needed from the embedded layer to power cloud‑based dashboards and real‑time alerts. Work across silos to ensure that a software glitch in a Zone Controller is seamlessly captured, uploaded to the cloud, and analyzed by an AI agent to guide a technician’s next‑best action. Design frameworks to evaluate how our AI systems interpret diagnostic evidence, ensuring grounding, traceability, and "explainability" in every repair recommendation. Requirements BS equivalent or higher degree in Computer Science, Systems Engineering, Electrical Engineering, or a related technical field. Minimum 3.5 cumulative GPA (or equivalent evidence of technical rigor). 1+ years of experience writing 2,000+ lines of clean, PEP8 compliant, modular Python code for data processing, API integration, or system automation. 1+ years of experience with Git-based version control (minimum 50+ commits/merges) and containerization (Docker), including deploying at least 3 containerized applications to a cloud or local environment. 1+ years of professional/research experience in at least 2 end‑to‑end AI/ML projects involving LLM orchestration (e.g., LangChain) or deploying a reasoning agent into a "live" state. 1+ years of experience processing and cleaning datasets exceeding 10,000+ records for model training or inference. 1+ years of experience translating ambiguity into structure by authoring at least 3‑5 detailed technical specifications (e.g., API contracts, System Requirements, or Sequence Diagrams). 1+ years of experience debugging complex systems (Embedded or Cloud), resolving at least 5‑10 high‑priority technical blockers using Root Cause Analysis (RCA). Experience leading at least 1 significant software module through the full lifecycle from initial requirements to a live, production environment with active users. 1+ years of experience using logic analyzers or tools (Wireshark/CANoe) to decode 3+ automotive protocols (e.g., CAN, DoIP, or SOME/IP). Experience building real‑time production dashboards in Grafana, Dynatrace, or Datadog to monitor system health and "drift." Experience authoring 5+ pieces of critical production documentation (FMEA, Interface Control Documents, or Production Validation Plans). Experience working on a system that handled 1,000+ concurrent nodes or data streams, demonstrating an understanding of horizontal scalability. #J-18808-Ljbffr Jobtailor