• AI/ML Pipeline Development: Design, develop, and maintain robust, scalable data pipelines specifically for our AI models. This includes data ingestion, cleaning, transformation, classification, and tagging to create high-quality, reliable training and evaluation datasets. • MLOps & Infrastructure: Build and manage the AI infrastructure to support the full machine learning lifecycle. This includes automating model training, versioning, deployment, and monitoring (CI/CD for ML). • Embedding & Vector Systems: Architect and operate scalable systems for generating, storing, and serving embeddings. Implement and manage vector databases to power retrieval-augmented generation (RAG) and semantic search for our AI agents. • AI Platform & Tooling: Champion and build core tooling, frameworks, and standards for the AI/ML platform. Develop systems that enable AI engineers to iterate quickly and self-serve for model development and deployment. • Cross-Functional Collaboration: Partner closely with AI engineers, product managers, and software engineers to understand their needs. Translate complex model requirements into stable, scalable infrastructure and data solutions. • Mentorship & Growth: Actively participate in mentoring junior engineers, contributing to our team's growth through technical guidance, code reviews, and knowledge sharing. • Hiring & Onboarding: Play an active role in interviewing and onboarding new team members, helping to build a world-class data engineering organization.