Senior AI Data Engineer
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Key skills for this role
About the Role
Scribd is looking for a Senior AI Data Engineer to lead AI engineering workstreams on the Data Platform team. You will build data infrastructure for AI use cases, support stakeholders in shipping data products faster with AI, and embed AI tooling into the development lifecycle.
Key Skills for This Role
Responsibilities
- 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
- Build AI skills and agents on top of existing declarative tooling — giving platform stakeholders the interfaces they need to ship pipelines faster
- Partner with teams across the organization to identify the right AI tools, frameworks, and agentic patterns that accelerate data product development
- 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
- Mentor other engineers and help define what great AI data engineering looks like at Scribd
Requirements
- 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)
- 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, and responsible handling of sensitive and PII data
- 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
Full Job Posting
About The Team
- 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 is a high impact team at the center of Scribd's AI adoption push.
About The Role
- We're looking for a Senior AI Data Engineer to lead the AI engineering workstreams on the Data Platform team.
- This role has three dimensions: building data infrastructure for AI use cases, supporting stakeholders in shipping data products faster with AI, and helping our own team accelerate development through AI tooling.
What You'll Do
- 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.
- Build AI skills and agents on top of our existing declarative tooling — giving platform stakeholders the interfaces they need to ship pipelines faster.
- 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.
You Have
- 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.
Nice to Have
- 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 eval harnesses, defining quality metrics, and catching regressions before they reach production.
- Working knowledge of terraform, or strong systems thinking to reason confidently about infrastructure changes without necessarily owning the code.
Compensation
- In Canada, the reasonably expected salary range is between CAD 162,000 and CAD 205,000.
- This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package.
Benefits At Scribd, Inc.
- Scribd Flex (flexible work model).
- Comprehensive health, dental, and vision coverage.
- Mental health support and disability coverage.
- Generous paid time off, including vacation, sick time, holidays, winter break, volunteer time, and sabbaticals.
- Paid parental leave and family support benefits.
- Retirement matching and employee equity.
- Learning and development programs and professional growth opportunities.
- Wellness and home office stipends.
- Complimentary access to the Scribd, Inc. suite of products.
- Enterprise access to leading AI tools.
Workplace Arrangement
- Scribd Flex empowers employees to choose the workstyle and location that support their best performance, while committing to intentional in person moments that strengthen collaboration and culture.
- Occasional in person attendance is required for all Scribd, Inc. employees, regardless of location.
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