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
