src/sample_inputs.py

RESUME_PATH = "/Users/gohyixian/Downloads/test_cases/CV_2024_24_JUN.pdf"

VIDEO_PATH = "/Users/gohyixian/Downloads/test_cases/test.mp4"

INTERVIEW_QUESTION = """
Can you describe a project where you fine-tuned a transformer-based model (e.g., BERT, GPT, or T5) for a specific application? 
Walk us through your approach to dataset preparation, model optimization, and deployment. 
How did you handle challenges like ensuring the model's performance, scalability, and fairness?
"""

JOB_REQUIREMENTS = """
Job Title: LLM Engineer

Job Description:
################
 - We are seeking a skilled and innovative LLM Engineer to join our AI team. The ideal candidate will 
   have hands-on experience in developing, fine-tuning, and deploying large language models (LLMs) for 
   various applications. You will collaborate with cross-functional teams to deliver cutting-edge AI 
   solutions, leveraging your expertise in natural language processing (NLP), deep learning, and 
   large-scale systems.


Key Responsibilities
####################
1.  Model Development:
    - Design and fine-tune large language models (e.g., GPT, LLaMA, or similar) for tasks like text generation, 
      summarization, question answering, and classification.
    - Implement advanced techniques for model optimization, including pruning, quantization, and distillation.

2.  Data Management:
    - Curate, preprocess, and manage large datasets for training and evaluation.
    - Ensure data quality by cleaning, augmenting, and annotating datasets.

3.  Infrastructure & Deployment:
    - Build scalable pipelines for training and deploying LLMs using frameworks like PyTorch, TensorFlow, or JAX.
    - Optimize inference speed and memory usage for production-grade applications.

4.  Model Evaluation:
    - Develop benchmarks to evaluate model performance, fairness, and safety.
    - Implement guardrails to mitigate bias and ensure ethical use of AI systems.

5.  Collaboration:
    - Work closely with product managers, data scientists, and software engineers to align model capabilities with business requirements.
    - Provide mentorship to junior team members and contribute to knowledge sharing within the team.

6.  Research & Innovation:
    - Stay updated on the latest research in NLP and deep learning.
    - Contribute to academic papers, patents, or open-source projects where appropriate.


Requirements
############
1.  Technical Skills:
    - Strong programming skills in Python.
    - Proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow, JAX).
    - Experience in training and fine-tuning transformer-based models (e.g., BERT, GPT, T5).
    - Familiarity with distributed training techniques and tools like Horovod or DeepSpeed.
    - Knowledge of vector databases and retrieval-augmented generation (RAG) techniques.
    - Hands-on experience with MLOps tools (e.g., MLflow, Docker, Kubernetes) for deployment.
    - Expertise in working with APIs for integrating LLMs into production systems.

2.  Educational Background:
    - Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Ph.D. preferred but not required.

3.  Experience:
    - 3+ years of experience in NLP, machine learning, or a related field.
    - Demonstrated success in building and deploying LLM-powered applications.
    - Contributions to open-source projects or research publications in NLP are a plus.

4.  Soft Skills:
    - Strong problem-solving abilities and attention to detail.
    - Excellent communication and collaboration skills to work with cross-functional teams.
    - Adaptable, with a passion for continuous learning and innovation.
    - A proactive and goal-oriented mindset.

5.  Target Personalities:
    - Innovative Thinker: Always exploring new ways to improve model performance and usability.
    - Team Player: Collaborates effectively across diverse teams to deliver AI solutions.
    - Ethically Minded: Committed to ensuring the ethical and fair use of AI technologies.
    - Detail-Oriented: Meticulous in coding, data handling, and model evaluation.
    - Resilient Learner: Thrives in a fast-paced environment, keeping up with advancements in AI research.


Preferred Qualifications:
#########################
- Experience with foundation model APIs (e.g., OpenAI, Hugging Face).
- Knowledge of reinforcement learning techniques, particularly RLHF (Reinforcement Learning with Human Feedback).
- Familiarity with multi-modal LLMs and their integration.
- Experience working in cloud environments like AWS, Azure, or GCP.
- Contributions to community forums, blogs, or conferences related to LLMs or NLP.

What We Offer
#############
- Competitive salary and benefits package.
- Opportunities to work on groundbreaking AI projects.
- Flexible work environment, including remote options.
- Access to cutting-edge resources and infrastructure for AI development.
"""