AI Architect – Enterprise Agentic AI Architecture

Job Purpose

Qualifications and Experience

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Data Science, or a related discipline.
• Senior technology professional with around 12+ years across software engineering, architecture, cloud or platform engineering, and enterprise solution delivery.
• At least 4+ years of hands-on AI engineering or AI architecture experience covering Generative AI, LLM applications, and Agentic AI solutions.
• Relevant certifications in cloud architecture, AI engineering, security architecture, enterprise architecture, or machine learning are preferred.

Experience Requirements

• Architecting and delivering multiple production-grade banking agents or full-fledged banking chat assistants integrated with enterprise systems.
• Strong understanding of banking systems, retail banking journeys, payments, transfers, servicing, self-service, operational controls, and regulatory or security considerations.
• Hands-on capability with Python, APIs, microservices, event-driven design, RAG, model or agent evaluation, and cloud-native deployment.
• Experience with agent frameworks, MCP servers, tool integration, A2A, A2UI, and agent interoperability patterns.

Technical Skills

• Enterprise Agentic AI architecture, multi-agent systems, autonomous workflows, human-in-the-loop design, and chat assistant architecture.
• LLMs, prompt engineering, context engineering, memory, tool or function calling, agent orchestration, evaluation, and cost or latency optimization.
• RAG architecture, semantic indexing, embeddings, vector databases, retrieval optimization, reranking, grounding, answer relevancy, and explainability.
• Azure AI, Azure OpenAI Service, AWS AI services, Amazon Bedrock, Kubernetes, serverless, microservices, APIs, event-driven architecture, and observability.
• AI security architecture including zero trust, PII redaction, data masking, privacy controls, guardrails, secure logging, auditability, and governance.

Leadership and Collaboration

• Present architecture options, risks, trade-offs, and recommendations to senior leadership and Architecture Review Board forums.
• Collaborate across business, product, engineering, cybersecurity, data, infrastructure, operations, compliance, and enterprise architecture teams.
• Provide pragmatic problem solving, ownership, mentoring, and strategic architecture direction for secure delivery.