:

Key Responsibilities

- Application Development: Build GenAI applications from scratch using frameworks like Autogen (applied or acquired), Crew.ai, LangGraph, LlamaIndex, and LangChain.
- Python Programming: Develop high-quality, efficient, and maintainable Python code for GenAI solutions.
- Large-Scale Data Handling & Architecture: Design and implement architectures for handling large-scale structured and unstructured data.
- Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models.
- Fine-tune SLM(Small Language Model) for domain specific data and use cases.
- Front-End Integration: Implement user interfaces using front-end technologies like React, Streamlit, and AG Grid, ensuring seamless integration with GenAI backends.
- Data Modernization and Transformation: Design and implement data modernization and transformation pipelines to support GenAI applications.
- Fine-Tuning LLMs: Apply fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLMs for specific use cases.
- LLMOps Implementation: Set up and manage LLMOps pipelines for continuous integration, deployment, and monitoring.
- Responsible AI Practices: Ensure ethical AI practices are embedded in the development process.
- innovation.

---

Required Skills

- Python Programming: Deep expertise in Python for building GenAI applications and automation tools.
- Productionization of GenAI application beyond PoCs – Using scale frameworks and tools such as Pylint,Pyrit etc.
- LLM Frameworks: Proficiency in frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.
- Large-Scale Data Handling & Architecture:



Design and implement architectures for handling large-scale structured and unstructured data.
- Multi-Modal LLM Applications: Familiarity with text chat completion, vision, and speech models.
- Fine-tune SLM(Small Language Model) for domain specific data and use cases.
- Prompt injection fallback and RCE tools such as Pyrit and HAX toolkit etc.
- Anti-hallucination and anti-gibberish tools such as Bleu etc.
- Front-End Technologies: Strong knowledge of React, Streamlit, AG Grid, and JavaScript for front-end development.
- Cloud Platforms: Extensive experience with Azure, GCP, and AWS for deploying and managing GenAI applications. (any two cloud exp.)
- Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods. (any one is fine)
- LLMOps: Strong knowledge of LLMOps practices for model deployment, monitoring, and management.
- Responsible AI: Expertise in implementing ethical AI practices and ensuring compliance with regulations.
- 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.
- OCR and Document Intelligence: Proficiency in OCR and document intelligence using cloud-based tools.
- API Integration: Experience with REST, SOAP, and other protocols for API integration.
- Data Curation: Expertise in building automated data curation and preprocessing pipelines.
- Technical Documentation: Ability to create transparent and comprehensive technical documentation.
- Collaboration and Communication: Strong collaboration and communication skills to work effectively with cross-functional teams.

Target Companies – Quantiphi,Datastax,Coforge,HCL,Accenture,Fractal.

📌 Gen AI (Bengaluru)
🏢 TalentOla
📍 Bengaluru