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Aissugur Kurganbekov
Claude Opus 4.8 (1M context)
Add recruiter-facing docs: case study, architecture, interview notes, demo, CV, outreach
b2f4ef1 | # Application Messages | |
| Short, role-specific notes for applications/DMs. Replace `[link]` with your repo | |
| or 60-second demo. Keep them under ~90 words. | |
| ## AI Product Intern | |
| Hi [Name] — I'm applying for the AI Product Intern role. I recently built | |
| FinanceFlow AI, a source-backed document assistant where every answer carries | |
| citations and the system abstains when evidence is missing. I also wrote a | |
| golden-dataset evaluation that measures citation coverage and unsupported-answer | |
| rate — treating a correct refusal as a success. It's the kind of trustworthy-AI | |
| product thinking I'd want to bring to your team. Demo + code: [link]. | |
| ## Product Operations Intern | |
| Hi [Name] — applying for the Product Operations Intern role. I built an | |
| end-to-end document-Q&A workflow (upload → retrieve → answer → cite → evaluate) | |
| that replaces manual PDF lookups, with logging and a measurable evaluation | |
| harness runnable from the UI and CLI and gated in CI. I'm comfortable turning a | |
| messy process into something measurable and reliable — happy to walk through it. | |
| Demo + code: [link]. | |
| ## AI Workflow Intern | |
| Hi [Name] — applying for the AI Workflow Intern role. I built FinanceFlow AI | |
| around the core document-AI loop: ingest → chunk → retrieve → answer → cite → | |
| evaluate, with guardrails (two abstention gates, always-on citations) that make | |
| it safe to put in front of users. It runs fully offline with an optional LLM | |
| layer. I'd love to apply the same grounded-workflow approach with your team. | |
| Demo + code: [link]. | |
| ## Backend Intern | |
| Hi [Name] — applying for the Backend Intern role. I built a typed, validated | |
| FastAPI service (9 endpoints, Pydantic, SQLAlchemy/SQLite, global error handling, | |
| file validation) with 20 tests and GitHub Actions CI, containerized with | |
| Docker Compose. It powers a retrieval-based document Q&A app. I care about clean, | |
| tested, well-documented services — code: [link]. | |
| ## ML / LLM Intern | |
| Hi [Name] — applying for the ML/LLM Intern role. I built a retrieval pipeline | |
| from scratch (tokenization, IDF weighting, length-normalized scoring, overlapping | |
| chunking) and a focused extractive answerer with citations, plus an optional | |
| OpenAI/Anthropic layer behind a strict grounding prompt. I evaluated it with a | |
| golden dataset (100% citation coverage, 0 unsupported answers). Code + write-up: | |
| [link]. | |