Sr. Systems Engineer - Design and RD

The Role

The Global Design and R&D IT team supports the design and research and development of GM production and concept vehicles, from early innovation through production design. We are seeking a highly motivated Sr. Systems Engineer to define, analyze, integrate, and evolve AI-enabled systems that support Global Design and Product Development engineering teams.

This is a blended systems engineering and systems analysis role. You will translate complex business and engineering needs into practical system solutions, evaluate opportunities to apply AI, and guide capabilities from discovery through production. You will work across requirements, architecture, data, APIs, applications, and operational processes with a strong focus on business value, security, quality, performance, and reliability.

This role is primarily focused on systems thinking, solution definition, integration, and technical leadership rather than day-to-day application development. You will partner with engineering, data, AI, architecture, and business teams to identify high-value AI opportunities, define system requirements and solution approaches, assess risks and dependencies, and guide delivery teams toward successful outcomes.

What You'll Do

• Lead discovery, systems analysis, solution definition, and continuous improvement for AI-enabled systems supporting Global Design and Product Development engineering teams.
• Facilitate discussions with business and technical stakeholders to understand needs, define problems, identify opportunities, and translate them into clear objectives and delivery plans.
• Identify and assess potential AI/ML and large-language-model (LLM) use cases, including business value, data readiness, feasibility, risk, security, privacy, and responsible-use considerations.
• Analyze current-state processes, applications, data flows, interfaces, and dependencies to identify gaps, risks, inefficiencies, and opportunities for modernization.
• Define future-state capabilities, system boundaries, integration patterns, solution options, and functional and non-functional requirements aligned with enterprise standards.
• Partner with data science, AI, software, and data engineering teams to guide AI capabilities from concept through production, including retrieval and inference integrations, evaluation, monitoring, guardrails, and continuous improvement.
• Define data requirements, data-quality expectations, lineage needs, validation rules, and data-access patterns for AI-enabled and data-intensive solutions.
• Define and evaluate API, integration, and interface requirements that connect data, business processes, AI services, and engineering workflows.
• Assess and recommend technology and architecture approaches across cloud platforms, databases, Databricks, APIs, containers, and AI services.
• Use working knowledge of SQL, Databricks, Java, and Python to evaluate solution designs, validate assumptions, analyze data needs, and collaborate effectively with implementation teams.
• Create system models, process flows, data flows, architecture diagrams, decision records, interface definitions, and other technical documentation that communicate solutions clearly to technical and non-technical audiences.
• Define acceptance criteria, test scenarios, traceability, validation approaches, and operational-readiness criteria in collaboration with product owners, developers, testers, data engineers, architects, and business stakeholders.
• Validate that delivered solutions meet functional requirements, data-quality expectations, security requirements, performance objectives, responsible-AI expectations, and intended business outcomes.
• Lead dependency, risk, issue, and change-impact analysis across systems, data, AI capabilities, and delivery teams.
• Ensure solution designs address appropriate security, privacy, observability, reliability, supportability, and governance requirements.
• Support production readiness and operational improvement through monitoring requirements, service-level objectives, incident analysis, release readiness, and continuous improvement.
• Lead or contribute to Agile/Scrum activities including discovery, backlog refinement, sprint planning, solution estimation, dependency management, and incremental delivery.
• Navigate enterprise processes, dependencies, and governance requirements to remove roadblocks and keep delivery on track.
• Provide technical guidance and mentoring to software engineers, systems analysts, data professionals, and other team members.
• Document system, data, AI, and integration solutions so that knowledge can be reused across business functions as applicable.

Your Skills & Abilities (Required Qualifications)

• Bachelor's degree in Systems Engineering, Computer Science, Software Engineering, Information Systems, Data Science, Engineering, or a related field, or equivalent experience.
• 6+ years of experience in systems engineering, systems analysis , solution architecture, product or platform engineering, data/AI delivery, or a related discipline.
• Demonstrated experience leading discovery and translating complex business and engineering needs into functional requirements, non-functional requirements, solution concepts, technical specifications, and actionable delivery plans.
• Experience analyzing and documenting business processes , current- and future-state capabilities, system interactions, data flows, interfaces, dependencies, and change impacts.
• Experience evaluating, integrating, or operationalizing AI/ML services, LLM APIs , or model-driven capabilities in enterprise or production environments, including evaluation, monitoring, security, and responsible-use considerations.
• Experience working with complex SQL queries , relational data, data transformations, data-quality requirements, and data-access considerations.
• Experience analyzing or supporting data pipelines, ETL/ELT processes, or data-intensive applications using Databricks , Spark , or comparable distributed data platforms.
• Experience defining or evaluating APIs, system integrations, cloud services, and enterprise integration patterns.
• Working knowledge of Java and/or Python sufficient to understand solution designs, assess technical tradeoffs, and collaborate effectively with software and data engineers.
• Understanding of cloud, containerized environments, security practices, observability, testing, release management, and operational-readiness concepts.
• Experience defining acceptance criteria, test scenarios, validation approaches, requirements traceability, and operational-readiness criteria for complex systems.
• Experience working in Agile/Scrum development methodologies, including discovery, backlog refinement, sprint planning, and incremental delivery.
• Strong analytical and problem-solving skills, with the ability to break down complex technical, data, AI, and business challenges into clear, actionable steps.
• Excellent written and verbal communication skills, with the ability to facilitate discussions and communicate effectively with technical and non-technical stakeholders.
• Demonstrated ownership, accountability for quality, and focus on delivering measurable value to internal customers.

What Will Give You a Competitive Edge (Preferred Qualifications)

• Master's degree in Systems Engineering, Data Science, Computer Science, Software Engineering, Engineering, Information Systems, or a related field.
• Experience defining AI product or platform capabilities , including use-case discovery, prompt or model integration, retrieval-augmented generation (RAG), agentic workflows, evaluation frameworks, guardrails, human-in-the-loop controls, or MLOps practices.
• Hands-on experience with Databricks , including Delta tables, notebooks, jobs, workflows, and AI/ML workloads.
• Hands-on experience using SQL, Python, or Java to prototype, validate, analyze, or support AI and data solutions.
• Experience with modern Java frameworks such as Quarkus and with enterprise application or integration platforms.
• Experience migrating applications, data workloads, or AI capabilities to Azure or other cloud platforms, including managed data, messaging, API, and AI services.
• Knowledge of relational and dimensional data modeling, data governance, metadata management, data lineage, and enterprise data-quality practices.
• Experience with systems modeling, architecture frameworks, requirements traceability, interface control documents, or comparable systems-engineering practices.
• Experience with observability and reliability practices, including logging, metrics, tracing, dashboards, alerting, and SRE concepts.
• Experience working with product lifecycle management ( PLM ), digital engineering, visualization, CAD/CAM, simulation, or design technology platforms.
• Experience supporting enterprise modernization, platform integration, or large-scale technology transformation initiatives.
• Experience with Docker, Kubernetes, CI/CD , or other modern software delivery practices.
• Demonstrated ability to influence technical direction, establish reusable patterns and standards, and mentor less experienced engineers and analysts.
• Ability to manage multiple initiatives, priorities, and stakeholder relationships in a fast-paced environment while maintaining high engineering standards.