Key skills
AWSAzureCloudDockerGoogle Cloud PlatformKubernetesAIMachine LearningMLRAGAgenticData EngineeringAnalyticsGCPGoogle Cloud
About this role
Role Overview
- Partner with automotive OEMs and Tier 1 suppliers to identify, shape, and prioritize high-impact AI-driven use cases, guiding customers toward scalable and high-leverage solutions
- Act as a trusted advisor to customers by shaping problem definition, challenging assumptions, and guiding technical decision-making toward scalable AI solutions
- Drive alignment and adoption of solutions by clearly articulating trade-offs, ROI, and long-term architectural implications to technical and executive stakeholders
- Design, integrate, and deploy AI/ML-based solutions across cloud and in-vehicle environments, enabling end-to-end vehicle-to-cloud use cases with reliable data flow and system interoperability
- Develop and iterate AI models, PoCs, and production-ready solutions, continuously improving performance based on real-world data and customer feedback
- Lead technical discussions and solution reviews focused on AI feasibility, model performance, trade-offs, and system limitations
- Identify risks related to AI deployment and integration (e.g., data availability, security constraints, model reliability) and drive mitigation strategies
- Collaborate with internal teams (AI Platform, Product, Engineering) to enhance AI capabilities, model deployment pipelines, and platform scalability
Requirements
- Bachelor’s degree in Computer Science, Engineering, or related field (Master’s or PhD preferred)
- 12+ years of experience in AI / Machine Learning, Data Engineering, or related domains, with exposure to automotive software or vehicle systems
- Solid understanding of machine learning, data analytics, and AI-based systems, including practical experience delivering real-world solutions
- Experience designing and working with data pipelines, analytics workflows, and model-driven systems
- Strong experience deploying and operating software in cloud environments (AWS, Azure, GCP, or customer-managed infrastructure)
- Experience integrating platform-based solutions with external systems, data pipelines, and APIs
- Familiarity with containerization and deployment technologies such as Docker and Kubernetes
- Strong hands-on experience developing, integrating, and deploying AI/ML-based solutions in production environments
- Proven ability to influence customer direction and drive technical decision-making in customer-facing engagements
- Experience leading technical discovery, shaping ambiguous problem spaces, and proposing scalable solution approaches
- Proven ability to communicate complex technical concepts clearly and persuasively to both technical and non-technical stakeholders
- Ability to operate effectively in ambiguous environments and drive clarity, alignment, and execution
- Experience in customer engineering, solution engineering, or forward-deployed engineering roles is highly preferred
- Familiarity with modern AI methodologies such as Agentic AI, Retrieval-Augmented Generation (RAG), or data-driven automation is a plus.
Tech Stack
- AWS
- Azure
- Cloud
- Docker
- Google Cloud Platform
- Kubernetes