JobFit AI

Candidates struggle to align their resumes with applicant tracking systems (ATS) and specific job descriptions, leading to high rejection rates due to missing keywords or poorly presented experience.

Designed and documented a contract-first resume optimization pipeline (Phase 0-13 roadmap) with a formal Domain Freeze, structured AI prompt schemas, and an ADR (Architecture Decision Record) change-control process.

An interactive consumer workspace where users paste a target JD and upload their existing resume. The platform reviews the alignment, scores compatibility, and outlines exactly which keywords, certifications, or experience records are missing — providing inline rewriting suggestions.

Strict contract-based design (docs/contracts/) where resume analysis pipelines, prompt formats, and JSON outputs are validated against frozen contract definitions.

• Parsing multi-column PDF layouts accurately to extract existing timeline experience
• Generating helpful, non-generic feedback that goes beyond simple keyword-stuffing

• Authored the complete 14-part architecture package and sequence design
• Defined the engineering rulebook and change control pipeline (ADRs)
• Built the visual system blueprints and schema validation scripts

• Automated resume-to-JD gap analysis detailing missing skills and experience gaps
• Actionable, inline keyword insertion suggestions optimized for modern applicant tracking systems (ATS)
• Contract-first backend architecture (contracts/) ensuring fast, deterministic parsing of unstructured PDF files
• Interactive progress checklist guiding users to improve their score before downloading their optimized resume

• A great AI tool doesn't rewrite the resume for the user; it guides the user with clear, explainable feedback on what to change.