The Head of Infrastructure, ADAS / Autonomous Driving is a senior leadership role responsible for designing, building, and operating scalable cloud and hybrid infrastructure to support ADAS and autonomous vehicle product delivery. This role owns the end‑to‑end infrastructure architecture, including cloud strategy, security models, DevOps practices, CI/CD and ML pipelines, and data governance for global deployments.

This is a hands‑on, builder‑focused role. The successful candidate will establish and grow an in‑house DevOps and infrastructure organization, define internal versus vendor ownership boundaries, and architect systems that enable reliable, secure, cost‑efficient, and compliant development of production ADAS and autonomous vehicle software. Success requires deep technical expertise, strong leadership, and experience operating in fast‑moving startup or growth environments.

You Will

• Own and define the end‑to‑end infrastructure and security architecture across cloud, on-premises, and hybrid environments.
• Design, deploy, and operate scalable cloud infrastructure and large‑scale data pipelines supporting sensor data ingestion, ML training, simulation, validation, and deployment for ADAS and autonomous vehicle systems.
• Lead efforts to modernize, optimize, and improve cost efficiency and performance of legacy infrastructure and data/ML pipelines, including identifying opportunities for architectural simplification and automation.
• Build and own CI/CD and ML pipelines supporting production AI and software workflows, with an emphasis on reliability, scalability, and efficiency.
• Implement Infrastructure‑as‑Code and automation frameworks for secure, repeatable deployments.
• Manage cloud infrastructure across AWS, OCI, Azure, or similar platforms including access control, cost governance, and resource optimization.
• Define ownership boundaries and partner effectively with IT and external vendors while retaining architectural control.
• Improve developer productivity and system reliability through tooling, observability, automation, and standardized development workflows.
• Ensure infrastructure and data platforms align with data privacy, security, and regional compliance requirements, including GDPR and region‑specific constraints for the EU, KSA, and other global markets.
• Define and track KPIs for reliability, scalability, cost efficiency, and developer experience.
• Collaborate cross‑functionally with Software, Product, Systems, Validation, Program, and Operations teams.
• Evaluate and adopt emerging infrastructure, DevOps, MLOps, and AI‑enabled tools where they provide clear value.

You Bring

• Hands‑on experience designing and operating cloud infrastructure architectures (AWS, OCI, Azure, or similar).
• Proven experience standing up and leading an in‑house DevOps or infrastructure organization.
• Strong experience with CI/CD pipelines and production ML workflows.
• Deep knowledge of Infrastructure‑as‑Code and automation tools such as Terraform and GitOps.
• Experience operating in hybrid environments (on‑premises and cloud).
• Experience working on cost optimization, performance tuning, and modernization of largescale or legacy infrastructure and data pipelines.
• Practical understanding of data privacy, security, and regulatory considerations, including experience handling data in regulated environments (e.g., GDPR, EU, Middle East/KSA).
• Builder mindset with experience in startup or growth‑stage environments.
• Experience supporting AI‑heavy, data‑intensive, or real‑time systems.
• Strong leadership, communication, and cross‑functional collaboration skills.
• Ability to independently design, validate, and defend infrastructure and security decisions.

Great to Have

• Experience across multiple areas of AI, such as machine learning platforms, data engineering, perception systems, or MLOps, beyond core infrastructure responsibilities.
• Platform software, middleware, or SDK development experience.
• Background in ADAS, autonomous vehicles, robotics, medical devices, or other safety‑critical domains.
• Experience with autonomous vehicle systems and physical sensor data such as cameras, LiDAR, radar, or GPS.
• Proficiency with containerized infrastructure and orchestration ecosystems, including Kubernetes, Docker, and Helm, as well as MLOps platforms and observability tooling.
• Familiarity with functional safety or automotive standards such as ISO 26262, ASPICE, or SOTIF.
• Cloud, DevOps, or Kubernetes certifications.

Education Requirements

• Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a related technical field.
• Master’s degree preferred.