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

Role Overview

Requirements

Tech Stack

Benefits

Sonatus

Open Positions (1)

Engineering

Senior Staff Customer Engineer – Automotive AI Engineer