We are looking for a talented Generative AI / Agentic AI Architect to design and deliver enterprise-grade AI solutions. This role involves creating intelligent banking assistants and AI agents utilizing LLMs, RAG, and multi-agent frameworks. You will lead the architecture, integration, and deployment of AI applications to enhance customer experiences and streamline business processes.

Ideal candidates possess strong Python skills, AI/ML framework expertise, and experience with LLM ecosystems and cloud-native architectures. Successful implementation of GenAI and Agentic AI in regulated environments is crucial.

Architect and implement GenAI and Agentic AI solutions, including intelligent assistants and autonomous AI agents. Design and deploy LLM-based applications using RAG, orchestration frameworks, and enterprise integrations. Collaborate with business, engineering, and data teams to deliver scalable AI solutions for banking use cases. Develop and optimize AI workflows for customer service, process automation, knowledge retrieval, and decision support. Integrate AI platforms with banking systems, APIs, enterprise data sources, and knowledge repositories. Establish AI governance, security, observability, and compliance controls aligned with regulatory requirements. Evaluate emerging GenAI and Agentic AI technologies and drive innovation through proofs of concept and production implementations. Define architecture standards, best practices, and reusable frameworks for enterprise AI adoption. Mentor technical teams and provide architectural leadership throughout the AI solution lifecycle.

Proficiency in Python and deep learning frameworks like TensorFlow or PyTorch. Strong understanding of generative models (VAEs, GANs, autoregressive models) and large language models (GPT-4, GPT-5). Experience with Azure OpenAI, Google Gemini, and developing generative AI models for various data types. Familiarity with transfer learning and pre-training techniques for LLMs. Skills in designing and developing RESTful APIs using Python and FastAPI. Expertise in prompt engineering for NLP tasks using few-shot, one-shot, and zero-shot learning. Understanding of Microsoft Azure services (Azure Functions, Storage, Cosmos DB, Machine Learning) and vector databases (ChromaDB, Elasticsearch) for RAG. Proven experience in architecting GenAI and Agentic AI solutions for intelligent customer interactions and process automation. Familiarity with AI governance and guardrails for regulatory compliance and responsible AI. Excellent communication skills for cross-functional collaboration. Conversational AI exposure is a plus.