Coding and Development: Write clean, using autogen and crew.ai frameworks efficient, and maintainable code for GenAI applications using Python and open-source frameworks.
Fine-Tuning Models: Fine-tune LLMs and SLMs using techniques like PEFT, LoRA, and QLoRA for specific use cases.
Open-Source Frameworks: Work with frameworks like Hugging Face, LangChain, LlamaIndex, and others to build GenAI solutions.
Cloud Tools Integration: Use cloud platforms (Azure, GCP, AWS) to deploy and manage GenAI models and applications.
Prototyping: Quickly prototype and demonstrate GenAI applications to showcase capabilities and gather feedback.
Data Preprocessing: Build and maintain data preprocessing pipelines for training and fine-tuning models.
API Integration: Integrate REST, SOAP, and other APIs for data ingestion, processing, and output delivery.
Model Evaluation: Evaluate model performance using metrics and benchmarks, and iterate to improve results.