On-line English Teaching Job NEW

Enke Education Technology Co., Ltd Job Description : On-line English teaching job.
Working time : 6:30-9:00 pm (Beijing time?during weekday , 8:00am-9:00 pm weekend.students age: 7-12 years old ,size (8-10kids ).

Requirements:
With good network,
The...
```
assistant
The provided HTML content includes various sections such as job listings, descriptions, and additional information about the job posting process. To extract the relevant fields and data from the HTML, we would typically use a web scraping tool or a parsing library like BeautifulSoup in Python. However, since I cannot execute code, I will describe how you could approach this task using a similar technique.

Here's a simplified example of how you might structure the response:

```python
# Assuming you have a list of job listings

job_listings = [
{
'company_name': 'Enke Education Technology Co., Ltd',
'monthly_salary_min': 250,
'monthly_salary_max': 35000,
'full_job_description': 'This is the full job description including benefits and requirements.',
'job_location': 'Beijing',
'job_type': 'Full Time, Part Time, Remote',
'experience_required': '?2 year(s)',
'work_city': 'Beijing'
},
# Additional job listings follow the same pattern
]

# Now, let's create a dictionary for each job listing
for job_listing in job_listings:
# Extracting specific fields
company_name = job_listing['company_name']
monthly_salary_min = job_listing['monthly_salary_min']
monthly_salary_max = job_listing['monthly_salary_max']
full_job_description = job_listing['full_job_description']
job_location = job_listing['job_location']
job_type = job_listing['job_type']
experience_required = job_listing['experience_required']
work_city = job_listing['work_city']

# Creating a nested dictionary for each job listing
job_details = {
'company_name': company_name,
'monthly_salary_min': monthly_salary_min,
'monthly_salary_max': monthly_salary_max,
'full_job_description': full_job_description,
'job_location': job_location,
'job_type': job_type,
'experience_required': experience_required,
'work_city': work_city
}

# Adding the job details to a list of job objects
job_object_list.append(job_details)
```

In this example, `job_listings` is a list of dictionaries containing the job details. Each job object in the list represents a single job listing, with keys corresponding to the fields being extracted. The values for these fields are stored in the corresponding value pairs within the job object.