Role Overview
As an AI Solution Architect, you will play a central role in advancing artificial intelligence capabilities within a leading automotive research and development organization. Your work will focus on shaping the technical direction for AI and machine learning initiatives, designing robust, end-to-end AI systems, and ensuring alignment with enterprise-wide governance and platform strategies. You will act as a bridge between technical innovation and business needs, delivering measurable improvements across operations and customer experiences.
Key Responsibilities
- Define and guide the technical AI/ML strategy, contributing to long-term vision and actionable roadmaps.
- Design and oversee the AI architecture landscape, ensuring scalability, integration, and compliance with enterprise standards.
- Collaborate with research, development, and business teams to identify high-impact use cases and implement data-driven AI solutions.
- Lead the development of generative AI systems using modern frameworks and architectures, including retrieval-augmented generation and agent-based models.
- Promote best practices in data science, DevOps, and agile delivery, ensuring consistent application across projects.
- Stay current with emerging AI technologies and translate advancements into practical applications for enterprise growth.
- Contribute to technical governance forums, share expertise, and help build internal AI capability.
Required Qualifications
- Advanced degree in a quantitative field such as computer science, mathematics, statistics, or engineering.
- Minimum of 8 years in software or solutions architecture, with at least 5 years focused on AI, machine learning, or generative AI in enterprise environments.
- Proven experience deploying large-scale AI systems, particularly in complex domains like automotive or manufacturing.
- Strong understanding of machine learning algorithms including classification, regression, clustering, and graph models, along with practical implementation experience.
- Familiarity with LLM architectures such as RAG and agent patterns, and tools including Langchain, Dify, and HuggingFace.
- Proficiency in Python, Java, Scala, C++, or C#, with solid software engineering practices.
- Hands-on experience with Spark, relational databases (e.g., MySQL, PostgreSQL, Oracle), and GraphDB technologies.
- Experience with DevOps, CI/CD pipelines, version control (Git), and agile methodologies in cross-functional teams.
- Background in multinational or multicultural work environments with strong collaboration skills.
- Experience in data visualization using Power BI or Python-based libraries.
- Strong analytical, statistical, and graph modeling skills applied to real-world business challenges.
