PROBLEM
Why this system exists
Traditional job boards list openings but do not explain how an opportunity aligns with a person’s skills, certifications, projects, experience, and career direction.
Backend AI Product Engineering
Active build
Agentic Career Intelligence Platform
A real backend product moving from job ingestion to explainable, persisted, personalized career recommendations.
An enterprise-style FastAPI platform with PostgreSQL, SQLAlchemy, Alembic, Redis, Docker, company and job services, deterministic scoring, career-profile APIs, personalized matching, persisted evidence, seed data, and smoke tests.
OUTCOME
What the build proves
Builds a reproducible career graph and stores versioned recommendation evidence so each personalized match can be inspected and explained.
IMPLEMENTATION PROOF
Evidence a technical reviewer can inspect.
Core implementation is in progress with working milestones and an explicit delivery roadmap.
Built capabilities
- Company, job, profile, skill, certification, project, and experience APIs
- Deterministic job scoring and persisted personalized match evidence
- Reproducible seed flow and smoke-tested API paths
Technology stack
PythonFastAPIPostgreSQLSQLAlchemyAlembicRedisDocker ComposeREST APIs
Evidence available
Backend validationHealth, CRUD, profile, match history, and global match endpoint checks
Persisted decision evidenceVersioned score, category, recommendation, strengths, and gap evidence
Architecture artifactIngestion, service, intelligence, data, and output layers
ENGINEERING BOUNDARIES
Precise claims build trust.
- The repository is private while product logic and data contracts continue to evolve.
- Automated source scanning, notification delivery, and the final dashboard remain planned layers.
RECRUITER / HIRING MANAGER
Need the architecture walkthrough?
I can explain the design decisions, implementation evidence, tradeoffs, and production-hardening path in a focused technical review.