Senior AI Data Engineer

Scribd, Inc. is on a mission to advance human understanding. Our four products — Scribd®, Slideshare®, Everand™, and Fable — help billions of people across the globe move beyond access and into insight, application, and expertise.

The Data Platform team sits within Scribd's Infrastructure organization and is responsible for the data infrastructure, governance, and enablement that powers the entire company. We own the foundational layers that make trusted data accessible — from our Medallion Architecture and Unity Catalog in Databricks, to the semantic and AI layers that sit on top. This high‑impact team is at the center of Scribd's AI adoption push, moving fast, setting standards, and ensuring data works for engineers, analysts, and business users alike.

We're looking for a Senior AI Data Engineer to lead the AI engineering work streams on the Data Platform team. This role has three distinct dimensions: building the data infrastructure that enables AI use cases across the company (AI in the data layer), supporting platform stakeholders in building and shipping data products faster with AI (AI in the data platform), and helping our own team accelerate development through AI tooling (AI in the development lifecycle).

• Own the deployment path for Databricks Apps — creating the infrastructure and guardrails that let non-technical users bring their AI applications to production safely and consistently.
• Build the AI layer on top of Scribd's Medallion Architecture and Semantic Layer — connecting AI agents and agentic workflows to governed data, and enabling non-technical users to get self‑service answers on top of it.
• Build AI skills and agents on top of our existing declarative tooling — giving platform stakeholders the interfaces they need to ship pipelines faster without deep expertise in every layer of the stack.
• Partner with teams across the organization to identify the right AI tools, frameworks, and agentic patterns that accelerate data product development and broader AI adoption.
• Identify and embed AI tools into how the Data Platform team writes, tests, and ships code — making AI‑assisted development a standard part of our engineering workflow.
• Establish guardrails to ensure AI‑generated code, queries, and pipelines are correct, consistent, and production‑ready.
• Help define and evolve data modeling and metadata patterns required to support AI use cases (e.g., context, documentation, discoverability).
• Mentor other engineers and help define what great AI data engineering looks like at Scribd.

We use Python, SQL, Databricks (Unity Catalog, Databricks Apps, Databricks SQL, DABs, Genie Spaces), Apache Airflow, Spark, Terraform/Spacelift, and AWS.

• 5+ years of data engineering experience, with at least 1 year focused on AI/ML infrastructure or LLM‑powered applications.
• Strong proficiency in Python and SQL; comfort working across the full data stack from ingestion to serving.
• Hands‑on experience with Databricks and cloud data platforms (Unity Catalog experience a strong plus).
• Experience building or integrating NLP/LLM‑based systems — RAG pipelines, semantic search, agent frameworks, or natural language interfaces.
• Working knowledge of how modern LLMs are trained, aligned and evaluated (RLHF, fine‑tuning, prompt engineering, retrieval patterns) and the judgment to know when each approach is the right tool.
• A solid understanding of data governance, access control, and what it means to build on top of trusted data.
• A security‑first mindset when building AI surfaces, including secret management, encryption at rest and in transit, and responsible handling of sensitive and PII data across AI pipelines and workflows.
• The ability to work autonomously on ambiguous problems and drive them to production.
• Strong communication skills — you can explain complex systems to technical and non‑technical audiences alike.

• Familiarity with metrics‑as‑code frameworks or semantic layer tooling (e.g., Statsig, dbt Semantic Layer).
• Prior experience in a platform or infrastructure team serving internal stakeholders.
• Experience evaluating AI model outputs, including building…