Generative AI

Role & responsibilities

GenAI (Must have - Autogen or CrewAI or WrenAI)

The Technical Lead will focus on the development, implementation, and engineering of GenAI applications using the latest LLMs and frameworks. This role requires hands-on expertise in Python programming, cloud platforms, and advanced AI techniques, along with additional skills in front-end technologies, data modernization, and API integration. The Technical Lead will be responsible for building applications from the ground up, ensuring robust, scalable, and efficient solutions.

Key Responsibilities:

1. Application Development: Build GenAI applications from scratch using frameworks like Autogen, , 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.

15. Data Curation Automation: Build tools and pipelines for automated data curation and preprocessing.

16. Technical Documentation: Create detailed technical documentation for developed applications and processes.

17. Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions.

18. Mentorship: Guide and mentor junior developers, fostering a culture of technical excellence and innovation.

Required Skills :

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, , 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.

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: Strong collaboration and communication skills to work effectively with cross-functional teams.

21. Mentorship: Proven ability to mentor junior developers and foster a culture of technical excellence.