Developer

1. Python Programming: Deep expertise in Python for building GenAI applications and automation tools.

2. Productionization of GenAI application beyond PoCs Using scale frameworks and tools such as Pylint,Pyrit etc.

3. LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.

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

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

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

7. Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc.

8. Anti-hallucination and anti-gibberish tools such as Bleu etc.

9. Front-End Technologies: Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development.

10. Cloud Platforms: Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications.

11. Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods.

12. LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management.

13. Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations.

14. RAG and Modular RAG: Advanced skills in Retrieval-Augmented Generation and Modular RAG architectures.

Data Modernization: Expertise in modernizing and transforming data for GenAI applications.