AI Architect

As a visionary Generative AI Architect, you will play a crucial role in leading the design and deployment of intelligent solutions by leveraging Large Language Models (LLMs), agentic frameworks, and advanced NLP techniques. Your expertise will be instrumental in building scalable generative AI systems across insurance, healthcare, and financial services. Your key responsibilities will include architecting modular GenAI solutions using LLMs such as GPT, Claude, LLaMA, and Mistral, with a strong focus on LangChain, LangGraph, and RAG. You will design and implement agent-based systems capable of reasoning, tool use, API integration, and memory management using LangChain agents and LangGraph-based workflows. Additionally, you will be responsible for designing robust, cloud-native AI platforms capable of handling large-scale data pipelines and complex machine learning models on platforms like AWS, Azure, and GCP. You will also be involved in developing and prototyping intelligent assistants, document automation systems, and NLP tools. Building robust RAG architectures with vector databases like FAISS and Pinecone and embedding models for domain-specific search and document intelligence will be part of your responsibilities. Furthermore, you will translate complex business problems into generative AI use cases in various areas such as underwriting automation, claims triage, policy analysis, and customer support. Collaborating with cross-functional teams, including data scientists, engineers, and domain SMEs, will be crucial to driving successful AI solution delivery. You will continuously evaluate and benchmark LLMs, agent orchestration tools, and GenAI platforms such as OpenAI, Hugging Face, and LlamaIndex. Optimizing performance and scalability for LLM-based systems deployed in production environments to ensure low latency, secure data handling, and ethical AI compliance will also be a key part of your role. Lastly, you will evangelize GenAI innovations internally and with clients through PoCs, demos, and technical workshops. In addition to the key responsibilities, it would be good to have experience developing structured AI pipelines using the MCP framework to clearly define model invocation logic, contextual understanding and memory, and input/output protocols for repeatable, enterprise-scale use cases. To qualify for this role, you should have at least 7+ years of experience in AI/ML, with a minimum of 3+ years specializing in NLP and Generative AI. You should also demonstrate proven hands-on expertise with LLMs such as GPT, Claude, LLaMA, Mistral, and BERT, including both open-source and commercial APIs like OpenAI and Anthropic. A deep understanding of LLM architecture and operations, along with proficiency in building Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models, is essential. Additionally, you should be proficient with GenAI frameworks, possess advanced programming skills in Python, and have experience deploying enterprise-ready AI solutions in production environments.,