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"Frontend Developer",
"Backend Developer",
"Full Stack Developer",
"AI/ML Engineer",
"Data Scientist",
"Data Analyst",
"Data Engineer",
"Unknown",
]
VALID_LEVELS = [
"Fresh",
"Junior",
"Senior",
]
ROLE_KEYWORDS = {
"Frontend Developer": [
"frontend",
"front-end",
"react",
"next.js",
"nextjs",
"vue",
"angular",
"javascript",
"typescript",
"html",
"css",
],
"Backend Developer": [
"backend",
"back-end",
"api",
"node.js",
"nodejs",
"express",
"fastapi",
"django",
"flask",
"spring",
"laravel",
"postgresql",
"mysql",
],
"Full Stack Developer": [
"full stack",
"fullstack",
"mern",
"mean",
"react",
"node.js",
"express",
"mongodb",
],
"AI/ML Engineer": [
"machine learning",
"ml engineer",
"artificial intelligence",
"ai engineer",
"deep learning",
"tensorflow",
"pytorch",
"llm",
"nlp",
"computer vision",
],
"Data Scientist": [
"data scientist",
"predictive modeling",
"feature engineering",
"statistics",
"scikit-learn",
],
"Data Analyst": [
"data analyst",
"power bi",
"tableau",
"excel",
"dashboard",
"reporting",
"sql",
],
"Data Engineer": [
"data engineer",
"etl",
"elt",
"data pipeline",
"airflow",
"spark",
"hadoop",
"warehouse",
],
}
def map_role(raw_role):
"""
Map extracted role text into one of supported system roles.
"""
if not raw_role:
return "Unknown"
role = str(raw_role).lower()
for canonical_role, keywords in ROLE_KEYWORDS.items():
if any(keyword in role for keyword in keywords):
return canonical_role
return "Unknown"
def infer_role_from_skills(skills, current_role=""):
"""
Infer the most likely supported role from title and detected skills.
"""
mapped_role = map_role(current_role)
if mapped_role != "Unknown":
return mapped_role
if not isinstance(skills, list):
return "Unknown"
skill_text = " ".join(str(skill).lower() for skill in skills)
scores = {role: 0 for role in ROLE_KEYWORDS}
for role, keywords in ROLE_KEYWORDS.items():
for keyword in keywords:
if keyword in skill_text:
scores[role] += 1
best_role = max(scores, key=scores.get)
return best_role if scores[best_role] > 0 else "Unknown"
def normalize_level(level, years_of_experience=0):
"""
Normalize the experience level to supported buckets.
"""
if isinstance(level, str):
lowered = level.strip().lower()
if lowered in {"fresh", "fresher", "intern", "entry", "entry-level"}:
return "Fresh"
if lowered in {"junior", "jr", "mid", "associate"}:
return "Junior"
if lowered in {"senior", "sr", "lead", "principal"}:
return "Senior"
if years_of_experience >= 3:
return "Senior"
if years_of_experience >= 1:
return "Junior"
return "Fresh"
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