AI Architect – Enterprise Agentic AI Architecture

JOB PURPOSE:

We are looking for an experienced AI Architect responsible for architecting enterprise-wide Generative AI and Agentic AI capabilities across banking systems. The role will define target architecture, integration patterns, standards, reference implementations and reusable building blocks for full-fledged AI chat assistants, autonomous agents and multi-agent workflows.

The AI Architect will lead end-to-end architecture for agentic retail banking journeys such as payments, transfers, servicing, self-service fulfilment and customer assistance, ensuring secure integration with enterprise APIs, middleware, core banking platforms and customer-facing channels across web and mobile.

This role requires deep hands-on AI engineering capability, strong banking domain understanding, practical delivery experience with multiple production-grade banking agents, and the ability to collaborate with product, engineering, infrastructure, cybersecurity, data, governance and enterprise architecture teams to present and align designs through ARB.

QUALIFICATIONS & EXPERIENCE:

Minimum Qualification

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Data Science or a related discipline.

Relevant certifications in cloud architecture, AI engineering, security architecture, enterprise architecture or machine learning are preferred.

Minimum Experience

Senior technology professional with around 12+ years of experience across software engineering, architecture, cloud/platform engineering and enterprise solution delivery.

Minimum 4+ years of hands-on AI engineering or AI architecture experience, including Generative AI, LLM applications and Agentic AI solutions.

Proven experience architecting and delivering multiple banking agents or full-fledged banking chat assistant capabilities integrated with enterprise systems.

Strong understanding of banking systems, retail banking journeys, payments, transfers, servicing, customer self-service, operational controls and regulatory/security considerations.

Full AI Engineer capability including Python, API integration, microservices, event-driven design, RAG implementation, model/agent evaluation and cloud-native deployment practices.

Hands-on experience with agent frameworks and orchestration platforms such as Microsoft Semantic Kernel, AutoGen, LangChain, LangGraph and similar frameworks.

Experience architecting MCP servers, tool integration layers, agent-to-agent communication, UI integration patterns and agent interoperability protocols such as MCP, A2A and A2UI.

Strong experience with Azure AI services, Azure OpenAI Service, AWS AI services, Amazon Bedrock and related model deployment/management capabilities.

Experience designing secure AI systems with zero trust principles, identity and access controls, data protection, secure API design, PII redaction and privacy-by-design controls.

Experience presenting solution architecture, trade-off analysis, ADRs and architecture recommendations to ARB or equivalent architecture governance forums.

Experience recommending infrastructure architecture for AI platforms including compute, Kubernetes, serverless, vector databases, observability, monitoring, data pipelines and connectivity.

Strong capability to collaborate with engineering, product, cybersecurity, infrastructure, operations, data, compliance and enterprise architecture teams.

Key Technical Skills

Enterprise Agentic AI architecture, multi-agent systems, autonomous workflows, human-in-the-loop design and full-fledged chat assistant architecture.

LLMs, prompt engineering, context engineering, memory design, tool/function calling, agent orchestration, model/agent evaluation and cost/latency optimization.

RAG architecture, semantic indexing, embeddings, vector databases, retrieval optimization, reranking, grounding, answer relevancy and explainability patterns.

MCP server architecture, tool registries, multi-tool integration, A2A, A2UI, agent interoperability protocols and AI ecosystem design.

Azure AI, Azure OpenAI, AWS AI services, Amazon Bedrock, Kubernetes, serverless, microservices, APIs, event-driven architecture and observability.

Security architecture for AI systems including zero trust, PII redaction, data masking, privacy controls, guardrails, secure logging and auditability.

Behavioural / Leadership Skills

Strategic architecture thinking with the ability to define enterprise standards, influence platform direction and simplify complex technical decisions.

Strong stakeholder communication with the ability to present architecture options, risks, trade-offs and recommendations to senior leadership and ARB forums.

Collaborative leadership style with the ability to work across business, product, engineering, cybersecurity, data and infrastructure teams.

Hands-on problem-solving mindset, pragmatic decision making, ownership, mentoring capability and commitment to high-quality secure delivery.

Technical Competencies

Enterprise Agentic AI architecture and banking-grade AI ecosystem design.

Retail banking agent architecture for payments, transfers, servicing and self-service workflows.

RAG, memory, context engineering, evaluation, tool orchestration and MCP server architecture.

AI security architecture, zero trust, PII redaction, guardrails, auditability and governance.

Azure and AWS AI services, cloud-native infrastructure, Kubernetes, serverless and observability for agent platforms.

Architecture documentation, ADR creation, ARB presentation and cross-team design governance.

Skills: AI Architecture , LLMs , Azure AI