Rayane Oubelkas
Applied AI Engineer | Agent Builder & Evals
I build reliable, auditable LLM agents and evaluation systems for high-stakes financial workflows, including multi-agent products across Excel, PowerPoint, and desktop automation.
Summary
Applied AI engineer and finance-domain founder building reliable, auditable LLM agents and evaluation systems for high-stakes financial workflows. Built multi-agent products across Excel, PowerPoint, and desktop automation.
Core capabilities
Applied AI: agent orchestration, multi-agent systems, evaluation harnesses, deterministic validators, tool and plugin frameworks, and MCP servers.
Engineering: Python, TypeScript, React, SQL, Rust (learning), Git/GitHub, and Excel and PowerPoint/Office add-ins.
Finance: financial modeling, LBO, DCF, comparable companies, REIT NAV, quantitative research, and equity research.
Experience
Krappa
- Built an Excel AI add-in with Office.js tools to create, audit, format, chart, and repair financial models; the system outperformed ChatGPT for Excel and Claude for Excel in internal agent evaluations.
- Built a PowerPoint AI add-in that converts HTML/CSS into editable finance and equity-research decks with native charts, source-backed templates, and automated slide QA; shipped 12 validated templates spanning 143 slides.
- Built Grinder, an Electron-based agentic finance workspace with primary-agent delegation to specialized subagents and secure access through Gmail, Outlook, WhatsApp Business, and Telegram.
- Integrated session-scoped Excel, PowerPoint, and PDF tools to create, edit, inspect, and validate financial models and presentations, with artifact tracking and safe file boundaries.
- Built a user-agentic plugin platform that lets users develop and install custom plugins bundling prompts, agents, skills, tools, workflows, hooks, context providers, scoped memory, and UI metadata.
- Built an internal agent-evaluation harness with 510 cases spanning DCF, LBO, three-statement, debt-schedule, and other financial-modeling tasks; measured pass rates, formula accuracy, formatting quality, tool-call count, token usage, latency, retries, and estimated API cost.
- Designed a leakage-resistant Qwen3.5-9B distillation pipeline combining verified tool SFT, skill-guided ReST, and preference/recovery training data for diverse financial-modeling tasks across industries, layouts, and model families.
- Generated and audited 35 industry/layout specialist skills and deterministic task/oracle families; enforced hash-bound checks for formulas, accounting integrity, artifact preservation, visual quality, screenshots, and provenance.
- Implemented 417 deterministic regression checks for accounting, formulas, reconciliation, spreadsheet integrity, and professional layout, preserving reproducible run traces.
Internal counts and comparisons are self-reported.
Locus Capital
- Built Python backtesting frameworks and applied quantitative market research and financial models to identify and validate investment opportunities.
- Built an AI market-research agent scheduled to run every three hours, retrieve market news through yfinance and financial data from the Financial Modeling Prep (FMP) API, and produce recurring research reports.
Tratop
- Prepared budgets and financial reports for a topographic studies office using financial modeling and data analysis.
- Built structured budget models to organize operating assumptions and project-level financial inputs.
- Analyzed financial data and converted findings into clear reporting summaries for internal review.
Softron Tax
- Prepared and filed simple personal income tax returns for individuals and families with modest incomes; provided clear, respectful, and community-centered guidance throughout the tax filing process; and ensured accuracy, compliance, and strict confidentiality when handling sensitive financial and personal information.
Education
Bachelor of Commerce - Finance (Co-op)
Bloomberg Market Concepts (BMC) Certificate - Bloomberg Institute
Writing
Last updated on 7/27/2026