Systems Engineer – End-to-End Software Diagnostics & Observability

Job summary

Define system requirements, interfaces, workflows, and observability for AI-enabled vehicle diagnostics, and support the development of capabilities for fault detection, root cause analysis, issue triage, and repair recommendations. Collaborate across embedded, cloud, AI/ML, and data teams on architecture, integration, testing, deployment, troubleshooting, and evaluation of diagnostic solutions.

Job details

Job title: Systems Engineer – End-to-End Software Diagnostics & Observability Location: ON- Kanata (Hybrid – 4 Days Onsite per Week) Duration: 12+ Months About the Role Ford Motor Company is transforming the future of mobility through software-defined, connected, and intelligent vehicles. As part of Ford's End-to-End Software Diagnostics & Observability initiative, we are building next-generation AI-powered diagnostic solutions that help engineering, diagnostics, and service teams rapidly identify, analyze, and resolve complex vehicle software and electronics issues. We are seeking a Systems Engineer – End-to-End Software Diagnostics & Observability to support the development of intelligent diagnostic workflows that integrate embedded vehicle systems, cloud platforms, observability tools, and advanced AI/ML technologies. This role is ideal for engineers passionate about AI, intelligent systems, diagnostics, and software-defined vehicles. Key Responsibilities Define and manage system-level requirements, interfaces, and workflows for AI-enabled vehicle diagnostics platforms. Translate business, service, and engineering needs into technical requirements and functional specifications. Support the development of AI-powered diagnostic capabilities for fault detection, root cause analysis, issue triage, and guided repair recommendations. Collaborate with embedded software, cloud, AI/ML, data engineering, and product teams to deliver scalable diagnostic solutions. Design and refine diagnostic evidence collection methods utilizing DTCs, PIDs, Freeze Frame data, vehicle logs, traces, and telemetry. Support AI-driven capabilities including knowledge retrieval, intelligent reasoning, decision support, workflow orchestration, and case intake automation. Participate in system architecture design, integration activities, testing, validation, and deployment efforts. Define observability requirements including logging, monitoring, metrics, dashboards, alerts, tracing, and escalation workflows. Evaluate AI system performance for accuracy, traceability, explainability, and operational effectiveness. Assist with cloud-native deployments and integration of containerized AI solutions within Ford-managed environments. Conduct root cause analysis and troubleshoot issues spanning embedded systems, cloud services, and AI applications. Communicate technical recommendations, trade-offs, and risks to stakeholders, leadership, and cross-functional teams. Required Qualifications Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Systems Engineering, Artificial Intelligence, Machine Learning, Data Science, Robotics, or a related discipline. 3 to 6 years of experience in Systems Engineering, AI/ML Engineering, Embedded Software, Cloud Engineering, or related technical domains. Strong understanding of systems engineering principles, software development lifecycle (SDLC), and systems integration. Strong proficiency in Python development. Familiarity with machine learning concepts, Large Language Models (LLMs), retrieval-augmented generation (RAG), inference systems, embeddings, ranking models, and reasoning workflows. Understanding of APIs, Git-based development, software architecture, and containerized application environments. Experience gathering requirements, defining workflows, and translating business needs into technical specifications. Excellent analytical, problem-solving, documentation, and communication skills. Demonstrated experience through professional work, internships, academic research, or projects involving AI/ML-enabled systems. Preferred Qualifications Experience with AI/ML frameworks such as PyTorch, TensorFlow, Scikit-learn, LangChain, or similar technologies. Hands-on experience with chatbots, copilots, AI assistants, semantic search, vector databases, agent-based systems, or decision-support platforms. Exposure to Google Cloud Platform (GCP), Vertex AI, BigQuery, Docker, GitHub, and CI/CD pipelines. Knowledge of observability tools such as Dynatrace, Grafana, OpenTelemetry, or similar platforms. Familiarity with embedded vehicle systems, automotive diagnostics, connected vehicle technologies, Electronic Control Modules (ECMs), DTCs, PIDs, and vehicle communication networks. Understanding of distributed systems, cloud-native architectures, and platform integrations. Experience evaluating AI systems for explainability, confidence scoring, policy compliance, and grounding. Ability to thrive in an Agile, collaborative, and highly technical environment. Required Technical Skills Systems Engineering Systems Analysis Systems Architecture Software Systems SDLC (Software Development Life Cycle) Product Management Python AI/ML Fundamentals Cloud Platforms (GCP Preferred) API Integration Requirements Engineering Root Cause Analysis Technical Documentation Preferred Technical Skills Artificial Intelligence & Expert Systems Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) LangChain PyTorch TensorFlow Vertex AI BigQuery Java Docker GitHub CI/CD Dynatrace Grafana Embedded Vehicle Diagnostics Work Arrangement: Hybrid (4 days onsite per week in I) Education: Bachelor's Degree Required | Master's Degree Preferred Experience Level: Mid-Level (3-6 Years) Industry: Automotive Technology, AI/ML, Embedded Systems, Cloud Engineering

What you’ll do

Define system requirements, interfaces, workflows, and observability for AI-enabled vehicle diagnostics, and support the development of capabilities for fault detection, root cause analysis, issue triage, and repair recommendations. Collaborate across embedded, cloud, AI/ML, and data teams on architecture, integration, testing, deployment, troubleshooting, and evaluation of diagnostic solutions.

Requirements

A bachelor's or master's degree in a relevant technical discipline and 3–6 years of experience in systems engineering, AI/ML, embedded software, cloud engineering, or a related field are required. Candidates should have strong systems engineering and Python skills, familiarity with AI/ML and LLM concepts, and experience translating requirements into technical specifications; automotive diagnostics, cloud, and observability experience are preferred.

Listed skills

• Python · Preferred
• Machine learning · Preferred
• Root Cause Analysis · Preferred
• Docker · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Systems Engineering
• Systems Analysis
• Systems Architecture
• Python
• Artificial Intelligence
• Machine Learning
• Large Language Models
• Retrieval-Augmented Generation
• Cloud Platforms
• Google Cloud Platform
• API Integration
• Requirements Engineering
• Root Cause Analysis
• Automotive Diagnostics
• Observability
• Docker

Job areas

• Engineering
• Technology
• Software
• Data & Analytics
• Manufacturing

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