AI/ML Architect

- Experience: Minimum 12+ years of total experience in software engineering, with at least 5-6 years focused on AI/ML and Natural Language Processing (NLP). - Technical Expertise:- Expert-level proficiency in Python and advanced SQL. - Deep understanding of Generative AI, Transformers, and LLM orchestration tools like LangChain or LlamaIndex.

Seeking a highly experienced Senior AI/ML Architect with over 12 years of professional experience to lead the design and development of our next-generation conversational AI platforms The ideal candidate will be a visionary in the field, possessing deep expertise in Text-to-SQL technologies and a proven track record of architecting scalable, enterprise-grade chatbot solutions. Core Responsibilities

Core Responsibilities - Architectural Leadership: Design end-to-end conversational AI architectures, integrating LLMs with enterprise data sources using Text-to-SQL and RAG (Retrieval-Augmented Generation) frameworks. - Text-to-SQL Development: Build and optimize high-accuracy Natural Language to SQL engines that enable non-technical users to query complex relational databases through chat interfaces.

- Architectural Leadership: Design end-to-end conversational AI architectures, integrating LLMs with enterprise data sources using Text-to-SQL and RAG (Retrieval-Augmented Generation) frameworks. - Text-to-SQL Development: Build and optimize high-accuracy Natural Language to SQL engines that enable non-technical users to query complex relational databases through chat interfaces. - System Integration: Connect AI models (OpenAI, Hugging Face) with internal databases, APIs, and cloud services (AWS, Azure, or GCP) ensuring scalability and security.

- Proven experience with deep learning frameworks such as PyTorch or TensorFlow. - Database & Data Engineering: Strong background in relational databases (PostgreSQL, SQL Server) and NoSQL/Graph databases for managing large-scale data streams. - Cloud Platforms: Hands-on experience with cloud AI services like AWS SageMaker or Google Cloud AI.

- Text-to-SQL Development: Build and optimize high-accuracy Natural Language to SQL engines that enable non-technical users to query complex relational databases through chat interfaces. - System Integration: Connect AI models (OpenAI, Hugging Face) with internal databases, APIs, and cloud services (AWS, Azure, or GCP) ensuring scalability and security. - Performance Optimization: Oversee the fine-tuning of Large Language Models (LLMs) and the optimization of prompt engineering strategies to improve system accuracy and response latency.

- Database & Data Engineering: Strong background in relational databases (PostgreSQL, SQL Server) and NoSQL/Graph databases for managing large-scale data streams. - Cloud Platforms: Hands-on experience with cloud AI services like AWS SageMaker or Google Cloud AI. - Education: Bachelor’s or Master’s degree in Computer Science, AI, or a related field.

- Deep understanding of Generative AI, Transformers, and LLM orchestration tools like LangChain or LlamaIndex. - Proven experience with deep learning frameworks such as PyTorch or TensorFlow. - Database & Data Engineering: Strong background in relational databases (PostgreSQL, SQL Server) and NoSQL/Graph databases for managing large-scale data streams.