AI Quality Engineer

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      "role": "<meta><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:7pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"font-size:14pt;white-space:pre-wrap;\">About the role</strong></b></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"white-space:pre-wrap;\">Luma Fintech is building a best-in-class LLM-powered document parsing pipeline that extracts structured data from complex financial product term sheets. We are seeking an AI Quality Engineer to own the daily testing, analysis, and iterative improvement of our Claude API-based extraction system. This role sits at the intersection of financial data operations and applied AI, you will be the person who closes the loop between what the model outputs and what the schema demands.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:7pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"font-size:14pt;white-space:pre-wrap;\">What you'll do</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Run daily accuracy evaluations against a defined extraction schema, tracking field-level performance across structured product types (autocallables, CLNs, barrier notes, etc.)</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Design and maintain test cases, regression suites, and gold-standard document sets to benchmark extraction quality over time</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Diagnose extraction failures, distinguishing between prompt logic issues, schema ambiguity, model hallucinations, and edge-case document formats</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Iterate on prompt engineering, system instructions, and context design to improve field-level extraction accuracy</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Work alongside the AI Engineer lead to feed findings into validation logic and rules-based layers that sit on top of LLM output</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Document failure modes with reproducible examples and root-cause hypotheses</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Build and maintain evaluation metrics (precision, recall, field coverage, hallucination rate) and report on accuracy trends</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Flag schema gaps or ambiguities surfaced by real document variance and collaborate with data operations to refine field definitions</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Contribute to RAG improvements by identifying where retrieved context is insufficient or misleading</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"font-size:14pt;color:rgb(32,32,34);white-space:pre-wrap;\">Qualifications</strong></b></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"color:rgb(32,32,34);white-space:pre-wrap;\">Required</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(32,32,34);--listitem-marker-color:#202022;font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(32,32,34);white-space:pre-wrap;\">Hands-on experience working with LLM APIs (Anthropic, OpenAI, or similar) in a production or near-production context</span></li><li style=\"color:rgb(32,32,34);--listitem-marker-color:#202022;font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(32,32,34);white-space:pre-wrap;\">Strong prompt engineering skills, you understand how instruction design affects model behavior, not just output tone</span></li><li style=\"color:rgb(32,32,34);--listitem-marker-color:#202022;font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(32,32,34);white-space:pre-wrap;\">Analytical mindset with the ability to systematically isolate variables in model output quality</span></li><li style=\"color:rgb(32,32,34);--listitem-marker-color:#202022;font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(32,32,34);white-space:pre-wrap;\">Experience designing structured test cases or evaluation frameworks (QA background is a plus)</span></li><li style=\"color:rgb(32,32,34);--listitem-marker-color:#202022;font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(32,32,34);white-space:pre-wrap;\">Familiarity with JSON schema, structured data output, and data validation patterns</span></li><li style=\"color:rgb(32,32,34);--listitem-marker-color:#202022;font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(32,32,34);white-space:pre-wrap;\">Ability to read and interpret complex financial or legal documents (term sheets, prospectuses, offering documents), prior financial services exposure strongly preferred</span></li><li style=\"color:rgb(32,32,34);--listitem-marker-color:#202022;font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(32,32,34);white-space:pre-wrap;\">Strong written communication; you’ll be documenting findings for both technical and non-technical stakeholders</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"color:rgb(32,32,34);white-space:pre-wrap;\">Preferred</strong></b></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(32,32,34);--listitem-marker-color:#202022;font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(32,32,34);white-space:pre-wrap;\">Experience with RAG pipelines and retrieval evaluation</span></li><li style=\"--listitem-marker-color:#202022;font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(32,32,34);white-space:pre-wrap;\">Python proficiency</span><span style=\"white-space:pre-wrap;\"> for scripting evaluation workflows or parsing outputs</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Background in structured financial products (autocallables, structured notes, credit-linked notes)</span></li><li style=\"font-size:11pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"white-space:pre-wrap;\">Familiarity with evaluation frameworks or tools (e.g., LangSmith, RAGAS, custom evals)</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:14pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"font-size:14pt;white-space:pre-wrap;\">What Success Looks Like</strong></b></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"white-space:pre-wrap;\">In 90 days, you have established a repeatable daily evaluation process, a documented baseline of field-level accuracy across product types, and have driven at least one measurable improvement in extraction quality through prompt iteration.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:14pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"font-size:14pt;white-space:pre-wrap;\">Why This Role</strong></b></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"white-space:pre-wrap;\">This is a high-ownership position on a strategic automation initiative with direct visibility to leadership. You won’t be maintaining someone else’s test suite, you’re building the quality layer of a system that processes real financial data at scale. The role will evolve as the system matures, with opportunity to expand into evaluation infrastructure and model improvement strategy.</span></p>",
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