ai engineer for digital mobility platforms

Mercedes-Benz.io develops software and technology for Mercedes-Benz digital platforms, including the worldwide Mercedes-Benz Website, an e-commerce platform, digital services, and aftersales solutions.

• Analyze and decompose complex problems, identifying where AI-driven reasoning adds value and where deterministic, testable software is preferable;
• Evaluate and select models, frameworks, and technologies based on quality, latency, scalability, cost, and production readiness;
• Design, build, and operate AI systems and agentic solutions, including single-agent and multi-agent architectures;
• Develop AI-powered applications using LLMs, prompts, structured outputs, tool-calling capabilities, and orchestration logic;
• Build retrieval and knowledge systems with retrieval pipelines, embeddings, chunking strategies, grounding mechanisms, and context management;
• Implement Responsible AI practices, including guardrails, privacy protections, security controls, auditability, human oversight, and regulatory compliance;
• Define and execute evaluation strategies through test datasets, quality assessments, performance measurement, regression testing, and failure analysis;
• Ensure production reliability and observability through monitoring, tracing, logging, metrics, performance management, and operational readiness practices;
• Investigate and resolve AI system failures across retrieval, prompting, context management, model behaviour, planning, and tool usage;
• Design recovery and fallback mechanisms to improve system resilience;
• Contribute to shared AI platforms and engineering practices by building reusable components, promoting standards, and sharing knowledge;
• Advocate for AI engineering excellence and help grow AI capabilities across teams through knowledge sharing, best practices, and technical enablement;
• Collaborate with stakeholders and domain experts to define requirements, validate outcomes, communicate limitations, and foster appropriate trust in AI-powered solutions.

• Proven software engineering experience designing, building, and operating maintainable, scalable, secure, and production-ready systems;
• Hands-on experience building AI-powered applications with LLMs, including prompting strategies, structured outputs, tool integration, or agentic workflows;
• Strong understanding of AI system design, agent architectures, orchestration patterns, state management, and tool integration approaches;
• Ability to evaluate technical and business trade-offs and select appropriate solutions;
• Experience designing and implementing retrieval and knowledge systems, including embeddings, retrieval pipelines, grounding strategies, context engineering, and RAG approaches;
• Knowledge of AI evaluation methodologies, including test design, quality assessment, regression testing, performance measurement, and failure analysis;
• Understanding of AI systems engineering practices, including observability, monitoring, reliability, troubleshooting, cost optimisation, and operational readiness;
• Knowledge of Responsible AI principles, including privacy, data protection, bias awareness, accountability, human oversight, and relevant AI regulations;
• Strong collaboration and communication skills with technical and non-technical stakeholders;
• Critical thinking, problem-solving ability, learning agility, continuous improvement mindset, and willingness to share knowledge and help others grow;
• English is the main working language.

• Flexible hybrid work model with an average of one day per week in the office;
• Open source software;
• No top-down hierarchy and a self-organized work environment;
• Health insurance and life insurance;
• International trainings and conferences for self-development;
• Brand Connection perks;
• Take-home challenge is part of the hiring process.