Find software engineers in SF with 10+ years of full-time experience

pip install exfunc openai

export EXFUNC_API_KEY=...
export OPENAI_API_KEY=...

from exfunc import Exfunc
from openai import OpenAI

exfunc = Exfunc()
openai = OpenAI()

search_people_response = exfunc.linkedin.search_people(request={
    "keywords": "software engineer",
    "locations": ["san francisco"]
})
people = [p.model_dump() for p in search_people_response.people][:20]

from concurrent.futures import ThreadPoolExecutor

def qualify_experience(experience):
    target_description = "full-time job"
    job_title = experience["title"]
    prompt = f'Answer True or False: Does "{job_title}" match the description: "{target_description}"?\n'
    messages = [{"role": "user", "content": prompt}]
    response = openai.chat.completions.create(messages=messages, temperature=0, model="gpt-4o-mini")
    completion = response.choices[0].message.content.strip()
    return "true" in completion.lower()

for p in people:
    experiences = p["experiences"]

    ft_experiences = []
    with ThreadPoolExecutor(max_workers=min(10, len(experiences))) as executor:
        for i, is_qualified in enumerate(executor.map(qualify_experience, experiences)):
            if is_qualified:
                ft_experiences.append(experiences[i])

    p["ft_experiences"] = ft_experiences

from datetime import datetime

def calculate_years_of_experience(experiences):
    total_months = 0

    for exp in experiences:
        start_date = datetime.strptime(exp['start_date'], "%Y-%m-%d")
        end_date = datetime.strptime(exp['end_date'], "%Y-%m-%d") if exp['end_date'] else datetime.now()
        
        # Calculate the number of months between start and end dates
        months_diff = (end_date.year - start_date.year) * 12 + (end_date.month - start_date.month)
        total_months += months_diff

    # Convert months to years
    years_of_experience = total_months / 12
    return years_of_experience

for p in people:
    p["ft_yoe"] = calculate_years_of_experience(p["ft_experiences"])

import pandas as pd

min_ft_yoe = 10

result = []
for p in people:
    if p["ft_yoe"] >= min_ft_yoe:
        result.append(p)

df = pd.DataFrame.from_records(result)
df[["url", "name", "location", "title", "company_name", "ft_yoe"]]