Core Expertise
Machine Learning
- Deep understanding of LLMs (e.g., GPT, LLaMA, PaLM) and their applications.
- Fine-tuning and deploying large language models.
- Managing multimodal LLMs with text and visual inputs.
- Strong knowledge of computer vision models (e.g., ResNet, YOLO, CLIP).
- Hands-on experience with multimodal AI applications, including image generation (e.g., Stable Diffusion, DALL·E).
- Expertise in handling large-scale datasets.
Natural Language Processing (NLP)
- Proficiency in NLP tasks such as summarization, translation, and question answering.
- Familiarity with performance optimization for large-scale AI models.
- Experience with text embeddings, vector search, and similarity models.
- Advanced tokenization techniques and preprocessing for text-based datasets.
- Designing and fine-tuning sequence-to-sequence models for specialized NLP tasks.
General Skills
- Proficient in using platforms for rapid prototyping and model optimization.
- Expertise in integrating computer vision models with LLMs to extract insights from images.
- Experience in collecting, preprocessing, and labeling multimodal datasets for training.
- Skilled in developing APIs and microservices to integrate LLMs into existing systems or build standalone applications.
Technical Knowledge
Frameworks and Libraries
- TensorFlow
- PyTorch
- Hugging Face Transformers
Data Analysis and Visualization
Tools for NLP Engineers
- Text Analysis and Preprocessing:
- NLTK
- SpaCy
- FastText
- Model Optimization and Deployment:
- ONNX
- TensorRT
- Ray Serve
- Data Management:
- DVC (Data Version Control)
- Apache Airflow
- Search and Retrieval:
- Elasticsearch
- Faiss
- Weaviate
- Performance Monitoring:
- Prometheus
- Grafana
- Sentry
Development Platforms
- Proficiency with platforms like Bytes AI or similar for LLM and multimodal AI development.
- Expertise in Python, with hands-on experience in ML frameworks and libraries.