Gen AI Architect

About the role

Cognizant is seeking an experienced Gen AI Architect to lead the design and delivery of enterprise‑wide Generative AI solutions. The role focuses on large‑scale transformation programmes, especially within banking and financial services, and requires strategic leadership across data, AI, and governance domains.

Key responsibilities

• Define and own the end‑to‑end GenAI solution architecture and target‑state blueprint for enterprise programmes.
• Design multi‑agent workflows for metadata ingestion, semantic mapping, impact analysis, validation and automation.
• Evaluate, select and optimise Large Language Models such as Claude and GPT‑based architectures to meet business and regulatory needs.
• Architect Retrieval‑Augmented Generation (RAG) solutions, including vector databases, embeddings, semantic search and knowledge retrieval frameworks.
• Establish prompt, context and orchestration strategies to maximise solution effectiveness and accuracy.
• Integrate with Amazon Bedrock, knowledge bases and related AI platform services.
• Develop enterprise AI governance frameworks covering explainability, transparency, auditability, risk management and compliance.
• Lead architecture governance forums, technical design reviews and solution assurance activities.
• Collaborate with data, cloud, security and application architecture teams to align with strategic technology roadmaps.
• Drive best practices for scalable, secure and responsible AI adoption across the organisation.

Required profile

• Proven experience designing and delivering enterprise Generative AI solutions.
• Deep knowledge of Agentic AI, LLM orchestration and RAG architectures.
• Strong background in enterprise data architecture and AI governance.
• Ability to work with senior business leaders, architects and engineering teams.
• Experience in banking or financial services environments is a plus.

Required skills

• Agentic AI
• Large Language Model (LLM) orchestration
• Retrieval‑Augmented Generation (RAG)
• Vector databases and embeddings
• Semantic search and knowledge retrieval frameworks
• Prompt engineering and context engineering
• Amazon Bedrock integration
• Claude and GPT‑based model optimisation
• Enterprise AI governance (explainability, transparency, risk management)