Title:  Architect

Area(s) of responsibility

Key Responsibilities:

1. Application Development: Build GenAI applications from scratch using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.

2. Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions.

3. Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.

4. Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models.

5. Fine-tune SLM(Small Language Model) for domain specific data and use cases.

6. Front-End Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends.

7. Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications.

8. OCR and Document Intelligence: Develop solutions for Optical Character Recognition (OCR) and document intelligence using cloud-based tools.

9. API Integration: Use REST, SOAP, and other protocols to integrate APIs for data ingestion, processing, and output delivery.

10. Cloud Platform Expertise: Leverage Azure, GCP, and AWS for deploying and managing GenAI applications.

11. Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases.

12. LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring.

13. Responsible AI Practices: Ensure ethical AI practices are embedded in the development process.

14. RAG and Modular RAG: Implement Retrieval-Augmented Generation (RAG) and Modular RAG architectures for enhanced model performance.