Principal AI ToolChain & Process Engineer

Organization-wide AI-Enabled R&D Strategy & Vision: Define and own the organization-wide AI-enabled R&D strategic vision, multi-year roadmap, and technology investment priorities, ensuring alignment with Mercedes-Benz global R&D objectives and business strategy. Present strategic recommendations to executive leadership (VP/CTO level) and drive cross-departmental consensus on AI adoption priorities and resource allocation. Establish governance frameworks for AI tool selection, evaluation, and deployment across the organization. Enterprise AI Toolchain Architecture & Platform: Architect and oversee the enterprise-level AI-powered R&D toolchain ecosystem, defining technical standards, integration patterns, and platform capabilities (e.g., AI programming assistants, intelligent test generation, AI-assisted

analysis, AI Agent-based project management, multi-agent collaborative development platforms). Make critical technology selection and architectural decisions, balancing innovation with enterprise requirements (security, scalability, compliance, cost). Drive build-vs-buy decisions and vendor/partner evaluation for AI toolchain components. Intelligent R&D Process Transformation: Lead organization-wide analysis and transformation of software development processes (requirements, design, coding, testing, release), identifying systemic bottlenecks and designing AI-powered solutions (predictive models, NLP, code generation, multi-agent systems) to fundamentally reshape working paradigms. Define and champion new AI-integrated development methodologies and process standards that become organizational best practices. R&D Efficiency Measurement & Strategic Insights: Architect and own the organization-wide data-driven R&D efficiency measurement framework, defining key metrics (delivery cycle time, defect density, code reuse rate, AI tool adoption rate, developer productivity index), building AI-powered analytics platforms, and providing executive-level strategic insights and visual reports to drive data-informed investment decisions. Technology Thought Leadership & Talent Development: Serve as the organization's technical thought leader in AI for software engineering, establishing the AI-driven R&D culture across departments, designing comprehensive training curricula, mentoring senior engineers and technical leads, and building an elite engineering organization capable of leveraging AI at scale. Represent Mercedes-Benz R&D at industry conferences, technical forums, and academic partnerships to establish external thought leadership and attract top talent. Technology Foresight, Research & Innovation: Lead the continuous evaluation and strategic assessment of emerging AI technologies in software engineering (LLM, AI Agent, AI4SE, autonomous coding systems), conducting advanced technical pre-research, prototype validation, and feasibility studies. Define the innovation pipeline and explore breakthrough application scenarios for automotive software development. Tech Committee Leadership & Cross-functional Governance: Lead and steer RDC Tech Committee activities, setting the technical agenda, driving architectural decisions, and ensuring alignment across teams on technology direction and people development. Establish and facilitate cross-functional technical review boards, architecture governance councils, and innovation steering committees. Drive continuous improvement of engineering practices, methodologies, and quality standards across the organization. Required Experience: 12+ years of experience in software development, architecture, or R&D efficiency platform-related roles, with deep expertise in ASPICE deployment in automotive projects. At least 5 years in a senior/lead technical role with demonstrated organizational-level impact. Proven track record of defining and executing AI-driven transformation strategies at scale across multiple teams or departments, with quantifiable results (e.g., leading enterprise-wide AI programming assistant adoption, establishing intelligent testing frameworks, building organization-wide automated code review pipelines, AI-assisted requirements management systems, and AI Agent-based workflow automation). Demonstrated experience in establishing technical standards, best practices, and architectural guidelines that have been adopted across an organization. Experience mentoring and developing senior engineers and technical leads, with a track record of growing technical talent. Technical Skills: AI/ML Capabilities: Expert-level understanding of machine learning/deep learning principles, with extensive hands-on experience and thought leadership in Prompt Engineering, AI Agent frameworks (e.g., LangChain, AutoGen, CrewAI), vibe coding, harness engineering, context engineering, RAG systems, and multi-agent orchestration. Deep expertise in multiple mainstream large language models (e.g., GPT series, Claude, CodeLlama, DeepSeek) including API integration, fine-tuning, RLHF, model evaluation, and enterprise-grade private deployment strategies. Proven ability to architect end-to-end AI solutions from proof-of-concept to production deployment at enterprise scale, including AI Agent application platforms and intelligent workflow systems. Experience in building and scaling AI/ML platforms and infrastructure to support organization-wide adoption. R&D Processes and DevOps: Expert-level understanding of modern software development processes and industry standards including ASPICE, Agile at Scale (SAFe), DevOps, CI/CD, and platform engineering. Ability to define and evolve organizational R&D process standards integrating AI capabilities. Data Skills: Strategic data-driven mindset with the ability to define organization-wide R&D efficiency measurement frameworks, architect data pipelines, design executive-level dashboards, and lead data-informed decision-making using advanced analytics platforms (e.g., SQL, Python, Tableau, Power BI) and AI-powered insights. Soft Skills: Exceptional communication and stakeholder management skills, with the ability to influence executive leadership and drive consensus across multiple departments and global teams (development, testing, architecture, project management, product management). Visionary technical leadership with strong strategic thinking, the ability to translate business objectives into technical initiatives, and a passion for pioneering new approaches to complex engineering challenges. Proven ability to build high-performing technical teams and foster an innovation-driven engineering culture. Preferred Qualifications: Experience as a principal/staff engineer or technical director leading enterprise-wide R&D efficiency platform construction or major digital transformation programs at large technology companies, internet companies, or smart device (mobile/automotive) manufacturers. Deep familiarity with automotive software development standards and characteristics (e.g., ASPICE, Functional Safety ISO 26262, AUTOSAR, SOTIF) and experience integrating AI solutions within safety-critical development environments. Published thought leadership (papers, patents, conference talks) in AI for software engineering or related domains. Master's degree or PhD in Computer Science, Artificial Intelligence, Software Engineering, or a related field.