Machine Learning Specialist Engineer

Responsibilities

• Model and curate the marketing semantic layer — governed metric definitions, tables ready for consumption by AI agents (e.g., Genie Spaces), and anti‑PII‑leakage rules. This is the backbone of the role, not a detail.
• Design data contracts and quality gates before writing transformations — ensuring that the data the model reads is correct, fresh, and traceable (groundedness starts with the data, not the prompt).
• Build and evolve scalable pipelines in Databricks (Workflows / DLT / Lakeflow), using AI‑assisted tools to speed up mechanical work and invest your time where real ambiguity exists.
• Unify multiple sources into a single customer view — transactional, behavioral, event, and API data — with low latency and consistency across activation channels.
• Prepare data for GenAI use cases — datasets for predictive models, preparation for RAG (chunking, embeddings, freshness), and context engineering for what agents...