# Example candidate profile. Copy to ~/.moonlighter/profile.yaml and fill with your
# real data. The LLM reads the scoring-relevant fields (criteria, skills, experience,
# preferences, summary, headline, languages) to evaluate and answer job applications.

name: "Jane Doe"
headline: "Senior Software Engineer"
location: "Remote (Brazil)"
phone: "+55 11 90000-0000"
email: "jane@example.com"
linkedin: "https://www.linkedin.com/in/janedoe"

languages:
  - "Portuguese (native)"
  - "English (fluent)"

summary: >
  Senior engineer with 10+ years building backend and full-stack systems.
  Strong in distributed systems, APIs and data-intensive services.

skills:
  - "Python"
  - "Go"
  - "PostgreSQL"
  - "Kubernetes"
  - "AWS"

experience:
  - company: "Acme Corp"
    role: "Senior Software Engineer"
    period: "2020 - present"
    highlights:
      - "Led the migration of a monolith to services, cutting p99 latency by 40%."
      - "Built the billing pipeline handling 2M events/day."

education:
  - degree: "BSc Computer Science"
    school: "Example University"
    year: 2014

publications: []

preferences:
  salary_min_usd: 150000
  remote: true
  relocation: false

# criteria drives the LLM score. hard_filters are non-negotiable dealbreakers:
# if any is triggered by a posting, the score is forced to <= 2.0.
criteria:
  hard_filters:
    - "requires .NET"
    - "on-site only (no remote)"
    - "US citizens only"
  soft_filters:
    - "prefer product companies over agencies"
  priority_targets:
    - "companies with a strong remote culture"