AI Architect – Enterprise Agentic AI Architecture
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Key skills for this role
Role Overview
DICETEK is seeking an experienced AI Architect to design enterprise-wide Generative AI and Agentic AI capabilities across banking systems.
The role covers AI chat assistants, autonomous agents, multi-agent workflows, and retail banking journeys.
The architect will collaborate across product, engineering, infrastructure, cybersecurity, data, governance, compliance, operations, and enterprise architecture teams.
Key Skills for This Role
Full Job Posting
Job purpose
DICETEK is seeking an experienced AI Architect to design enterprise-wide Generative AI and Agentic AI capabilities across banking systems.
The role covers AI chat assistants, autonomous agents, multi-agent workflows, and retail banking journeys.
The architect will collaborate across product, engineering, infrastructure, cybersecurity, data, governance, compliance, operations, and enterprise architecture teams.
Architecture responsibilities
- Define target architecture, integration patterns, standards, reference implementations, and reusable building blocks for enterprise AI capabilities.
- Lead end-to-end architecture for payments, transfers, servicing, self-service fulfillment, and customer assistance.
- Design secure integrations with enterprise APIs, middleware, core banking platforms, and web and mobile channels.
- Present architecture options, trade-offs, ADRs, and recommendations to architecture review boards.
- Recommend infrastructure architecture covering compute, Kubernetes, serverless, vector databases, observability, data pipelines, and connectivity.
Qualifications and experience
- Bachelor's or master's degree in a relevant computing, engineering, artificial intelligence, or data science discipline.
- Around 12 or more years of experience across software engineering, architecture, cloud or platform engineering, and enterprise solution delivery.
- At least four years of hands-on AI engineering or AI architecture experience with Generative AI, LLM applications, and Agentic AI.
- Proven delivery experience with multiple banking agents or full-fledged banking chat assistants integrated with enterprise systems.
- Relevant cloud, AI, security, enterprise architecture, or machine learning certifications are preferred.
AI and architecture skills
- Python, API integration, microservices, event-driven design, RAG, model or agent evaluation, and cloud-native deployment.
- Agent frameworks and orchestration platforms including Semantic Kernel, AutoGen, LangChain, and LangGraph.
- MCP servers, tool integration layers, agent-to-agent communication, A2A, A2UI, and agent interoperability protocols.
- LLMs, prompt engineering, context engineering, memory design, tool calling, evaluation, and cost or latency optimization.
- RAG architecture, semantic indexing, embeddings, vector databases, retrieval optimization, reranking, grounding, relevancy, and explainability.
Banking domain
Strong understanding of banking systems, retail banking journeys, payments, transfers, servicing, self-service, operational controls, and regulatory or security considerations.
Experience architecting secure banking-grade AI ecosystems and customer-facing agent solutions.
Cloud and platform skills
- Experience with Azure AI, Azure OpenAI Service, AWS AI services, and Amazon Bedrock.
- Experience with Kubernetes, serverless, microservices, APIs, event-driven architecture, and observability.
AI security
Secure AI design using zero trust, identity and access controls, data protection, secure APIs, PII redaction, privacy-by-design, data masking, guardrails, logging, auditability, and governance.
Leadership and collaboration
Strategic architecture thinking, stakeholder communication, collaborative leadership, hands-on problem solving, pragmatic decision making, ownership, and mentoring capability.
The role requires influencing platform direction and presenting risks, trade-offs, and recommendations to senior leadership and architecture governance forums.
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