GenAI Technical Architect
Birlasoft Limited
8-12 Yrs
Bangalore
Not Disclosed By Recruiter
Bachelor Degree/ Master Degree
Autogen CrewAI WrenAI LLMOps Python Programming
Key Responsibilities:
- Apply advanced fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLM performance for specific use cases.
- Build scalable and modular architecture for GenAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.
- Guide and mentor junior developers and engineers, fostering a culture of innovation and technical excellence.
- Make detailed design artifacts, technical specifications, and architecture diagrams for complex projects.
- Implement Reinforcement Learning with Human Feedback (RLHF) and Retrieval-Augmented Generation (RAG) techniques to enhance model performance.
- Participate with front-end developers to integrate GenAI capabilities into user-friendly interfaces.
- Leverage Hugging Face and other open-source platforms for model development, fine-tuning, and deployment.
- Lead the development of Python-based GenAI applications, ensuring high-quality, maintainable, and efficient code.
- Ensure ethical AI practices by implementing Responsible AI principles, including fairness, transparency, and accountability.
- Evolve and optimize Modular RAG architectures for complex GenAI applications.
- Establish and manage LLMOps pipelines for continuous integration, deployment, and monitoring of LLM-based applications.
- Build tools and pipelines for automated data curation, preprocessing, and augmentation to support LLM training and fine-tuning.
- Should have a strong understanding of LLMOps practices for model deployment, monitoring, and management.
- Should have good experience in Python for making GenAI applications and automation tools.
- Should be proficient in frameworks like Autogen, Crew, ai, LangGraph, LlamaIndex and LangChain.
- You should have advanced skills in reinforcement learning with human feedback and retrieval-augmented generation.
- Excellent experience in designing and implementing APIs for GenAI applications.
- Should be Expertise in building automated data curation and preprocessing pipelines.
- Should have a good ability to make clear and comprehensive design artifacts and technical documentation.
- Knowledge of front-end technologies to enable seamless integration of GenAI capabilities.
- Deep understanding of Modular RAG architectures and their implementation.
- Extensive experience with Azure, GCP, and AWS LLM ecosystems and APIs.
- Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods.
- Proven ability to lead teams, mentor junior developers, and drive technical innovation.
- Experience in designing and implementing APIs for GenAI applications.
- Strong understanding of secure software development lifecycle and DevSecOps practices for LLMs.
- Expertise in implementing ethical AI practices and ensuring compliance with regulations.