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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.
"""
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