Experience shipping testedA track record of starting thingsSolid understanding of data pipelinesFamiliarity with metadata systemsExperience with search relevanceAgentic engineering in practice
Required
Strong proficiency in Python (data processing, API development, and integrations).
Hands-on work with LLM-based and AI-driven enrichment models (e.g., classification, entity extraction, deduplication, PII detection).
Production experience with Spark or comparable big data frameworks — you've tuned and debugged jobs at real scale, not just written ones that worked on sample data.
Experience shipping tested, reviewed production services rather than notebooks — and the discipline to hold that line when a coding agent writes the first draft.
A track record of starting things: a specific example where you took a vaguely-scoped problem, defined the MVP yourself, wrote down your assumptions, and shipped it — without a PM converting it into tickets first.
Solid understanding of data pipelines, microservice architecture, and API design.
Experience ingesting and processing data from third-party enterprise sources (e.g., SharePoint/OneDrive, Salesforce, and SaaS-based knowledge bases).
Familiarity with metadata systems, data cataloging, or document AI workflows.
Knowledge of model evaluation best practices.
Experience with search relevance.
A bachelor’s degree or equivalent related working experience is required.
Agentic engineering in practice, not just tool usage: you can say where an agent loop earns its keep, what guardrails and structured outputs keep it in bounds, and how you manage its context. Same discipline when coding agents write for you — tests, review gates, repo conventions.
