Jobs at APTIV

Perform system-level verification and validation of automotive electronic products, including BCMs, gateways, key fobs, and RFR systems. Design test cases, analyze requirements, configure automated and manual HIL test benches, execute tests, report defects, and support issue analysis. The role requires knowledge of automotive communication and diagnostic protocols, electrical circuits, electronic controller architectures, network compliance testing, and requirements management tools.

Own end-to-end mechanical and electromechanical product design, development, validation, and release for automotive low- and high-voltage electrical centers. Create Siemens NX models, assemblies, drawings, specifications, and BOMs; apply GD&T, tolerance analysis, packaging, and DFM/DFA/DFT principles. Manage configuration control, supplier and tooling reviews, PPAP, testing, corrective actions, and production launch readiness while collaborating with cross-functional engineering, manufacturing, quality, purchasing, and supplier teams.

Build and operate AWS-based MLOps platforms for autonomous-driving workloads. Maintain highly available multi-zone environments, distributed multi-GPU Ray training clusters, and infrastructure for compute, storage, networking, and security. Develop Airflow and MLflow pipelines, support GitHub-based CI/CD for ML code, models, and infrastructure, troubleshoot platform issues, and ensure workflows are reproducible, traceable, and auditable.

Manage QNX-based software development projects, optimize boot times and system performance, implement testing strategies, and integrate hypervisor technologies.

Lead shift-level execution of Quality, Cost, Delivery targets for an automotive assembly area. Manage and optimize assembly line performance, supervise operators and technicians, enforce safety and 5S, drive quality control and root-cause corrective actions, apply preventive maintenance, and ensure material availability to meet production plans.

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.

The Engineering Project Leader will manage design reviews, technical risk assessments, handle customer change requests, and ensure engineering deliverables comply with standards while collaborating with various engineering disciplines over a 14-18 year experience span.