About the role
AI summarisedThe Principal AI Architect will design and build enterprise-grade AI applications and frameworks for Mobility Aftermarket, combining deep hands-on AI engineering with business process understanding. Responsibilities include architecting AI systems, developing end-to-end AI-driven applications, integrating AI into operational workflows, defining technical roadmaps, and mentoring AI engineers. The role requires strong coding skills in Python, experience with ML/LLM systems, cloud-native architectures, and familiarity with automotive/mobility business processes.
IndustrialOnsiteAnalyst
Key Responsibilities
- Define and evolve the AI application architecture for Mobility Aftermarket
- Design reusable AI components and frameworks
- Establish standards for model integration, deployment, monitoring, and scaling
- Ensure AI systems are production-grade and enterprise-ready
- Design and build AI-driven applications end-to-end
- Implement ML models, LLM-based workflows, APIs and microservices, and orchestration layers
- Develop self-service and reusable AI agents
- Translate Mobility Aftermarket business processes into AI logic
- Embed AI into real operational workflows such as Order-to-Cash, customer interaction flows, trade operations, and business insight
- Balance AI innovation with business feasibility
- Evaluate AI use cases for feasibility and scalability
Requirements
- 8–12+ years in AI/ML engineering or applied AI
- Strong hands-on coding experience with Python (mandatory)
- Experience building AI applications from scratch
- Experience deploying AI solutions into production environments
- Solid understanding of ML model lifecycle, LLM integration, API architecture, microservices, and cloud-native systems
- Experience embedding AI into enterprise workflows
- Understanding of ERP-driven business processes
- Ability to translate business requirements into AI system design
- Experience in Automotive/Mobility/Trade environment (preferred)
- Experience building reusable AI components
- Knowledge of MLOps and AI monitoring
- Experience designing scalable AI architectures
- Familiarity with responsible AI and governance
- Deep technical curiosity and strong system thinking
- Ability to challenge both business and technical teams
- Mentorship mindset without formal people management