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

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