Jobs at APTIV

Delivers customer-facing engineering solutions for vehicle systems and connected services. Responsibilities include developing requirements, analyzing vehicle-level issues, performing ECU and powertrain diagnostics, acquiring and validating data, supporting simulation and on-vehicle testing, identifying root causes, and coordinating issue resolution. The role also produces engineering reports, presentations, and project timelines while collaborating with customers and cross-functional teams.

Coordinates engineering change requests from initial assessment through approval and implementation. Facilitates technical and organizational review meetings, prepares agendas and summaries, supports engineers using CMII and GES, and ensures compliance with documentation, configuration management, quality, and change-control standards. The role also contributes to engineering databases and tools while collaborating with cross-functional teams.

Designs and develops multilayer PCBs for high-performance, reliable, high-volume products. Responsibilities include schematic and BOM creation, PCB layout, high-speed interface support, stack-up analysis, SI/EMI/EMC and thermal validation, manufacturing output generation, Valor verification, tooling documentation, and circuit simulations. The role collaborates with global engineering teams, participates in design reviews, supports production documentation, and ensures compliance with Aptiv quality standards.

Support, enhance, and maintain a web-based automotive business intelligence platform. Investigate and resolve production issues across distributed systems and legacy codebases, collaborate with analysts and developers, implement improvements, validate fixes through acceptance testing, and improve system reliability, performance, and development efficiency. The role involves backend development, microservices, software testing, debugging, and technical documentation.

Develops and optimizes electrical manufacturing functional tests, test software, hardware, and tooling for automotive product programs. The role manages test requirements, coverage analysis, equipment procurement and validation, capability studies, statistical analysis, documentation, training, and global test-process support. Requires technical education, programming skills, automotive communication protocol knowledge, statistical methods expertise, and strong problem-solving abilities.

Maintains and develops PCB component libraries, including schematic symbols and geometry cells. Creates, reviews, modifies, and approves library components while supporting design for manufacturing and compliance with electrical design and quality processes. Collaborates with local and remote engineering teams, executes assigned tasks on schedule, protects confidential information, and continuously develops technical expertise. The role also supports automotive PCB design and may involve high-speed simulation and electronic circuit design.

Designs and operates scalable batch and streaming data pipelines, ETL/ELT frameworks, data models, APIs, and cloud-native services supporting ADAS and autonomous-driving solutions. Ensures data quality, security, governance, observability, and reliability across AWS or Azure platforms. Partners with AI/ML and MLOps teams to deliver production-ready datasets and features, while contributing to architecture, CI/CD, infrastructure automation, technical standards, and mentoring.

Design, build, test, and operate cloud-based data ingestion and ETL systems. Develop backend services in Golang (with Python where appropriate), run workloads on Kubernetes/AWS, troubleshoot distributed systems, and collaborate to improve scalability, reliability, and performance.

Build, operate, and maintain MLOps platforms on AWS for autonomous driving workloads. Implement multi-zone, highly available training and deployment environments, operate distributed multi-GPU training (Ray), and maintain ML pipelines (Airflow, MLflow). Support CI/CD for ML code and infrastructure, ensure reproducible, traceable, auditable ML workflows, and troubleshoot platform-level issues.

Build and operate MLOps platforms on AWS for autonomous-driving ML workloads. Maintain highly available multi-zone training and deployment environments, run distributed multi-GPU training (e.g., Ray), implement Airflow orchestration and MLflow model lifecycle, support GitHub-based CI/CD for code/models/infrastructure, and ensure reproducible, traceable, auditable ML workflows.

Lead MLOps platform and infrastructure for autonomous-driving ML: build multi‑AZ AWS/Kubernetes environments, operate distributed multi‑GPU training, implement Airflow/MLflow pipelines, CI/CD, data ingestion/validation, monitoring, and collaborate with ML teams to deliver safety‑critical production ML systems.