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: Robust 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. (any two cloud exp.)
11. Fine-Tuning Techniques:
Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. (any one is fine)
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.
15. Data Modernization: Expertise in modernizing and transforming data for GenAI applications.
16. OCR and Document Intelligence: Proficiency in OCR and document intelligence using cloud-based tools.
17. API Integration: Experience with REST, SOAP, and other protocols for API integration.
18. Data Curation: Expertise in building automated data curation and preprocessing pipelines.
19. Technical Documentation: Ability to create clear and comprehensive technical documentation.
20. Collaboration and Communication: Stron
📌 Generative Ai (Mumbai)
🏢 HR SOLUTIONS
📍 Mumbai
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