Senior DevOps Engineer - Edge AI & Robotics

About Aptiv Aptiv is a global technology leader with more than 200,000 employees across 48 countries. We develop advanced software, compute platforms, and intelligent systems that enable autonomous driving, advanced driver-assistance systems (ADAS), connected mobility, and intelligent infrastructure. Aptiv technology is embedded in millions of vehicles worldwide and powers some of the industry's most advanced automotive platforms. As part of our expansion into Robotics and Adjacent Markets, Aptiv is developing scalable software and embedded computing platforms for autonomous mobile robots (AMRs), drones, industrial automation, and next-generation intelligent machines. These platforms combine embedded software, sensing, connectivity, autonomy, safety-critical systems, and cloud-connected architectures to enable reliable operation in demanding real-world environments. As a Lead DevOps Engineer – Edge AI & Robotics , you will design, implement, and operate the automation, infrastructure, and deployment pipelines that enable scalable software delivery for robotics, drones, autonomous mobile robots, and intelligent edge platforms. You will work closely with software, AI/ML, embedded systems, and product teams to improve development productivity, release quality, deployment reliability, and operational visibility across cloud, edge, and embedded environments. Key Responsibilities DevOps, CI/CD & Release Engineering Design, implement, and maintain CI/CD pipelines for robotics, embedded software, AI/ML, and edge platform development. Automate build, test, integration, packaging, release, and deployment workflows across multiple software components and hardware targets. Establish scalable release engineering practices including versioning, artifact management, dependency control, and traceability. Collaborate with development teams to improve build reliability, pipeline performance, test coverage, and software delivery velocity. Develop automation scripts, reusable pipeline templates, and standardized DevOps workflows for global engineering teams. Support secure software delivery practices including access control, secrets management, dependency scanning, and compliance reporting. Drive continuous improvement of development infrastructure, toolchains, and release readiness processes. Cloud, Edge Infrastructure & Platform Automation Build and operate development, integration, and deployment infrastructure for cloud, edge, and embedded computing environments. Create infrastructure-as-code solutions and automation for reproducible environments, test benches, and deployment targets. Support containerized workflows using Docker and orchestration technologies such as Kubernetes where applicable. Enable reliable deployment of software artifacts to Linux-based edge devices, embedded targets, simulation systems, and validation platforms. Implement monitoring, logging, and observability practices to improve system visibility, troubleshooting, and operational readiness. Software Quality, Tooling & Engineering Productivity Partner with software, AI/ML, embedded, systems, and validation teams to define efficient development and integration workflows. Improve software quality through automated build verification, unit tests, integration tests, static analysis, and regression testing. Support toolchain integration for source control, issue tracking, requirements management, artifact repositories, and test reporting. Develop dashboards and reports that provide visibility into pipeline health, release status, build quality, and test results. Enable reproducible development environments for local development, CI runners, simulation platforms, and target hardware. Support compliance with applicable software development processes, cybersecurity expectations, and quality standards. Troubleshoot build, test, deployment, infrastructure, and environment issues across distributed engineering teams. Robotics, Edge AI & Embedded Deployment Support deployment workflows for AMRs, drones, industrial automation systems, and intelligent edge devices. Automate packaging and deployment of robotics software, middleware, AI/ML models, drivers, and platform services. Work with robotics teams using technologies such as Linux, ROS 2, DDS, Docker, simulation environments, and embedded hardware platforms. Enable reliable integration between sensors, compute platforms, software services, test infrastructure, and cloud-connected systems. Support hardware-in-the-loop, simulation-in-the-loop, and target deployment workflows for validation and continuous testing. Basic Qualifications Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or a related discipline. 3-5 years of experience in DevOps, CI/CD, release engineering, software infrastructure, or platform engineering. Hands-on experience building and maintaining CI/CD pipelines using tools such as GitHub Actions, GitLab CI, Jenkins, Azure DevOps, or similar. Strong experience with Linux environments, scripting, automation, and software build systems. Experience with Docker, containerized development workflows, and artifact management. Experience with infrastructure-as-code, configuration management, or automated environment provisioning. Proficiency in scripting languages such as Python, Bash, or PowerShell. Understanding of software engineering principles, version control, branching strategies, testing workflows, and release processes. Strong troubleshooting, communication, and cross-functional collaboration skills. Preferred Qualifications 5+ years of experience in DevOps, platform engineering, release engineering, or software infrastructure. Experience supporting embedded software, robotics, AI/ML, edge computing, or automotive software development environments. Experience with Kubernetes, Helm, container registries, and scalable build or deployment infrastructure. Experience with cloud platforms such as Azure, AWS, or Google Cloud and hybrid cloud-edge workflows. Familiarity with ROS 2, DDS, robotics middleware, simulation environments, or distributed embedded systems. Experience with hardware-in-the-loop, simulation-in-the-loop, or automated validation infrastructure. Experience integrating static analysis, security scanning, test automation, and quality gates into CI/CD pipelines. Familiarity with requirements management, traceability, Automotive SPICE, functional safety, or cybersecurity development processes. Integrate security scanning solutions such as Coverity or equivalent technologies Knowledge of embedded platforms from NVIDIA, Qualcomm, NXP, Renesas, AMD, Intel, TI, or similar vendors. Experience with MLOps practices and frameworks such as MLflow, DVC, Kubeflow, Airflow, Weights & Biases, KServe, Seldon Core, ONNX, TensorRT, model registries, experiment tracking, model packaging, deployment automation, and monitoring of AI/ML workloads on cloud and edge platforms. Traits We Value Automation-first mindset with a passion for improving developer productivity and software delivery speed. Strong ownership mentality with a bias toward reliable execution and continuous improvement. Ability to work across software, AI/ML, embedded, systems, validation, and product teams. Pragmatic problem-solving skills with the ability to troubleshoot complex build, deployment, and infrastructure issues. Interest in robotics, drones, autonomous systems, edge computing, and intelligent machines. Collaborative mindset with strong communication and stakeholder engagement skills. Desire to build scalable engineering platforms that enable the next generation of autonomous and intelligent systems. You grow at Aptiv. Aptiv's winning culture is global by design. We bring together diverse perspectives, cultures, and business contexts to solve our customers' toughest challenges. In this environment, every individual can grow, lead, and make an impact, regardless of background, because inclusion fuels how we inn