Role overview
- Design, oversee, build and operate data pipelines and applied AI systems that power our education
platform across consumer and business customer segments. - Build and scale retrieval and search foundations (hybrid search, metadata filtering, vector retrieval)
that enable AI experiences. - Deliver production LLM capabilities with measurable quality, cost controls, and safety guardrails.
- Improve system performance, reliability, observability, and maintainability through proactive
monitoring and data quality checks. - Collaborate closely with stakeholders (product managers, designers, frontend/mobile engineers) to
translate business requirements into technical solutions. - Contribute to sprint planning, technical documentation, and cross-team initiatives as a senior
engineering voice. - Contributing to sprint planning, technical documentation, and cross-team initiatives as a senior voice
in the engineering org
- 4+ years of experience in data engineering, backend engineering, or platform engineering, with meaningful production ownership.
- Strong SQL and data modeling skills; experience designing schemas and data contracts.
- Experience building and operating pipelines (batch and/or streaming), including backfills and data
quality validation. - Experience with search/indexing systems and retrieval patterns (e.g., OpenSearch/Elasticsearch,
vector DBs, hybrid search). - Hands-on experience implementing LLM-powered features (RAG, tool/function calling,
- prompt/version management) — no deep ML required.
- Familiarity with CI/CD, containerization (Docker), and cloud services (ex. AWS, GCP).
- Strong problem-solving and debugging skills, with a proactive attitude towards owning and
improving systems. - Strong written and verbal communication skills, with the ability to explain complex ideas clearly to
technical and non-technical stakeholders. - Bachelor's degree in computer science, Engineering, or a related field is preferred but not required
Tags & focus areas
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