Computing Intern

Computing Intern

Job Type:
Internship

Duration:
3-6 months (with potential for extension based on performance)

About Us:
Backbone is a forward-thinking firm specialising in innovative solutions for the accounting industry. We are seeking a highly motivated Intern to join our team and contribute to the development of cutting-edge AI technologies.

Key Responsibilities:

• Assist in the design and development of AI-driven tools for accounting automation.
  
• Work on enhancing AI capabilities for our existing systems.
  
• Collaborate with the development team to integrate AI functionalities.
  
• Participate in testing and validation of AI models.
  
• Document development processes and provide regular updates.
  

Required Skills and Knowledge:

• Educational Background:
  Currently pursuing or recently completed a degree in Computer Science, Data Science, AI, Machine Learning, or a related field.
  
• Technical Skills:
  
  • Programming Languages:
    Proficiency in Python.
    
  • Machine Learning:
    Experience with developing and training machine learning models. Familiarity with libraries such as TensorFlow, PyTorch, and scikit-learn.
    
  • Natural Language Processing (NLP):
    Knowledge of NLP techniques and libraries such as NLTK, spaCy, or Hugging Face Transformers.
    
  • API Integration:
    Experience with RESTful APIs and working knowledge of integrating third-party APIs.
    
  • Data Handling:
    Proficiency in handling structured and unstructured data, and experience with databases (SQL/NoSQL).
    
  
  
• Preferred Skills:
  
  • Experience with Azure Cognitive Services or OpenAI services for NLP.
    
  • Familiarity with cloud platforms such as Azure for deploying AI models.
    
  • Knowledge of financial data and accounting principles.
    
  
  
• Soft Skills:
  
  • Strong problem-solving skills and attention to detail.
    
  • Excellent communication and teamwork abilities.
    
  • Ability to work independently and manage time effectively.
    
  • Seeking undergraduates who are interested in attachment opportunities only