Sr Architect

Key Responsibilities

• Design and Architecture: Create scalable and modular architecture for GenAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.
• Python Development: Lead the development of Python-based GenAI applications, ensuring high-quality, maintainable, and efficient code.
• Data Curation Automation: Build tools and pipelines for automated data curation, preprocessing, and augmentation to support LLM training and fine-tuning.
• Cloud Integration: Design and implement solutions leveraging Azure, GCP, and AWS LLM ecosystems, ensuring seamless integration with existing cloud infrastructure.
• Fine-Tuning Expertise: Apply advanced fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLM performance for specific use cases.
• LLMOps Implementation: Establish and manage LLMOps pipelines for continuous integration, deployment, and monitoring of LLM-based applications.
• Responsible AI: Ensure ethical AI practices by implementing Responsible AI principles, including fairness, transparency, and accountability.
• RLHF and RAG: Implement Reinforcement Learning with Human Feedback (RLHF) and Retrieval-Augmented Generation (RAG) techniques to enhance model performance.
• Modular RAG Design: Develop and optimize Modular RAG architectures for complex GenAI applications.
• Open Source Collaboration: Leverage Hugging Face and other open-source platforms for model development, fine-tuning, and deployment.
• Front-End Integration: Collaborate with front-end developers to integrate GenAI capabilities into user-friendly interfaces.
• SDLC and DevSecOps: Implement secure software development lifecycle (SDLC) and DevSecOps practices tailored to LLM-based projects.
• Technical Documentation: Create detailed design artifacts, technical specifications, and architecture diagrams for complex projects.
• Stakeholder Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions.