Job Description

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, 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.
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, 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.
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.

Skills Required