Company: Gerber Collision & Glass Great Teams Don’t Happen byAccident Built with Intent. Driven by YOU. At the Boyd Group, our teams work to provide the elite infrastructure and supportive environment you need to Be the Best and outperform at every touchpoint in collision and glass services. As we continue to grow and lead the industry, we ensure you have the resources and the team behind you to move your career forward. Ready to grow with a team that’s built for your success? Apply today. Our Commitment: The Boyd Group welcomes unique talents from all backgrounds and characteristics. We act with integrity and appreciate the diverse perspectives that make our "Greater Team" exceptional. Qualified individuals, including those with disabilities and Protected Veterans, are encouraged to apply. Job Description Job Summary The AI & Data Architect is a high-impact role responsible for accelerating our AI maturity and optimizing the data ecosystem that supports it. This role focuses on defining and implementing foundational elements for AI adoption - Architecture patterns (Gen-AI, RAG, MCP, Agentic workflows etc.), tooling, process & governance in collaboration with other stakeholders. This role will be key contributor to broad architecture of high-performance Data & BI platforms and solutions, ensuring Data platforms are key enablers of AI adoption journey The role reports to the Head of Ent. Architecture and is responsible for working very collaboratively with the Head of Data & BI platform, in order to develop and implement a coherent AI & data strategy. Key Job Responsibilities Strategy, Roadmap & Innovation • Strategy: In collaboration with Head of Enterprise Architecture, Data Engineering and BI to develop and execute AI & Data Strategy, in ensuring technical goals align with broader business objectives. • Roadmap Development: Build and maintain a multi-year AI/Data Roadmap, identifying key milestones for capability maturity, tool adoption, and infrastructure upgrades. • AI Solution Design: Design and implement architectural patterns for Generative AI, including Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and Agentic AI frameworks. • Innovation & Modern DevEx: Explore the impact of AI on the software lifecycle, including AI-driven testing and emerging trends like "Vibe Coding" to accelerate development velocity. • Prototyping & PoCs: Lead Proof-of-Concept projects to evaluate new AI tools and techniques, moving successful experiments into production-ready environments. Data Platform & Governance • Modern Data Stack: Help define the evolution of the Data Platform architecture (AWS data ecosystem - S3, Lake Formation, Redshift, Airflow, Lambda) to ensure it is performant and "AI-ready." • Architecture Governance: Define and enforce best practices for AI security, prompt engineering standards, and the ethical use of machine learning models. • Pipeline Architecture: Design scalable ETL/ELT pipelines and data modeling strategies that support both real-time AI needs and historical BI reporting. • BI Optimization: Oversee the architectural health of our BI layers (e.g., Domo) to ensure dashboards and self-service analytics are powered by clean, governed data. Minimum Education and/or Experience Required for the Job • AI Expertise: 2–3 years of hands-on experience with AI/ML implementations, focusing on GenAI patterns and LLM orchestration. • Data Engineering: 5+ years of experience in data architecture or engineering, with deep knowledge of data warehousing, lakehouses, and distributed processing. • Cloud & Tools: Strong proficiency in the AWS Data Stack, Python, and SQL. • Collaborative Execution: Ability to work with engineering teams to build the "plumbing" while working with business stakeholders to deliver the "intelligence." Required Knowledge, Skills, & Abilities • Industry Knowledge: awareness of the top AI solution providers and the rapidly evolving ecosystem of LLM providers and AI startups. • Advanced AI Patterns: Familiarity with AI-driven Software Development Life Cycle (SDLC) enhancements and testing automation. • Data & BI Ecosystem: Knowledge of leading platforms beyond the core stack, including: Data Platforms: Snowflake, Databricks, or Azure Data Lake, BI Tools: Power BI, Tableau, or Qlik. • Emerging Trends: Understanding of the shift toward natural language programming/orchestration (e.g., Vibe Coding) and standardized context sharing (MCP). Preferred Education and/or Experi