I’m creating a proof-of-concept résumé generator aimed at veterans transitioning to civilian careers and I need a Python specialist to turn the concept into a clean, test-driven MVP. Web app (Streamlit) • Single entry-point module, app.py, that renders forms for contact details, MOS codes, and target roles. • Real-time translation of each entered MOS into civilian skills/keywords, with a live preview the user can export as JSON. • Integrated AI: auto-generate résumé summary (2–3 sentences) and STAR-style bullet options with “Regenerate/Apply” controls (editable in UI). • Strong Pydantic v2 validation on every submit; no auth or persistence at this stage. • DOCX download from the UI (ATS-friendly template). (PDF not required for MVP; add only if trivial within scope.) Command-line tool • build_resume.py reads the JSON profile and, via Jinja2 + docxtpl, outputs a polished DOCX using the same AI + mapping logic as the UI. • Templating is easy to extend and supports simple branding tweaks (logo, color palette, accent color). Architecture & quality gates • Clean separation of concerns: core/ modules for mapping_service, resume_service, and ai_service (provider-agnostic; env-flagged; mock provider for tests). • Deterministic output: stable ordering, fixed template styles, and a golden-text test (extract DOCX → normalize whitespace → compare). • Thorough tests with pytest + coverage; lint/type checks with ruff/black/mypy. • Inline docstrings and a concise README (setup, run commands, how to add MOS mappings/templates, AI config). • Privacy: no PII persisted; all data in session/memory only. Deliverables 1. app.py Streamlit project with minimal CSS/theme assets 2. build_resume.py CLI and one ATS-friendly docxtpl template (templates/classic/Resume.docx) 3. Test suite (unit + integration with mock AI) and coverage report 4. README and sample inputs (data/mos_mapping.csv + profile.sample.json) If this aligns with your expertise in Streamlit, Pydantic v2, Jinja2/docxtpl, and DOCX generation/testing, please share a brief architecture/testing approach (including DOCX determinism + AI mock), examples of prior DOCX/Jinja work, and a fixed price that fits a ~20-hour MVP.