Architect

Area(s) of responsibility

The Implementation Technical Architect will be responsible for designing, developing, and deploying cutting-edge Generative AI (GenAI) solutions using the latest Large Language Models (LLMs) and frameworks. This role requires deep expertise in Python programming, cloud platforms (Azure, GCP, AWS), and advanced AI techniques such as fine-tuning, LLMOps, and Responsible AI. The architect will lead the development of scalable, secure, and efficient GenAI applications, ensuring alignment with business goals and technical requirements.

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