Title:  Gene AI Architect _SubContractor

Area(s) of responsibility

GenAI Technical Architect (Must have  - Autogen,CrewAI and WrenAI)

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.

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..

Required Skills:

Python Programming: Deep expertise in Python for building GenAI applications and automation tools.
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.
Cloud Platforms: Extensive experience with Azure, GCP, and AWS LLM ecosystems and APIs.
Fine-Tuning Techniques: Mastery of PEFT, QLoRA, LoRA, and other fine-tuning methods.
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.
RLHF and RAG: Advanced skills in Reinforcement Learning with Human Feedback and Retrieval-Augmented Generation.
Modular RAG: Deep understanding of Modular RAG architectures and their implementation.
Hugging Face: Proficiency in using Hugging Face and similar open-source platforms for model development.
Front-End Integration: Knowledge of front-end technologies to enable seamless integration of GenAI capabilities.
SDLC and DevSecOps: Strong understanding of secure software development lifecycle and DevSecOps practices for LLMs.