The Enterprise Architect - Agentic AI and Delivery Lifecycle will define and lead the enterprise architecture for the company's adoption of agentic AI across Product, Software Engineering, and Platform Engineering.

The role will establish the company-wide Agentic Delivery Lifecycle (ADLC) and govern the platforms, security controls, reusable capabilities, observability, and cost disciplines required to deploy AI agents safely at enterprise scale. The successful candidate will combine enterprise architecture leadership with hands-on expertise in AI agents, large language models, software development tooling, Model Context Protocol (MCP), cloud platforms, and regulated financial-services environments.

Key Responsibilities

1. Enterprise Agentic Delivery Lifecycle and Adoption Roadmap
• Establish and govern the ADLC for agent design, development, testing, approval, deployment, production access, and monitoring, supported by enterprise policies, standards, and reusable patterns.
• Define the adoption roadmap across Product Management, Software Engineering, and Platform Engineering, with risk-based controls and approval gates reflecting autonomy, data sensitivity, system access, financial impact, and regulation.

2. Agentic Platform and Infrastructure Architecture
• Architect, implement, and govern enterprise agentic platforms, including OpenAI Codex, Anthropic Claude Code, and approved alternatives, with secure model access, routing, identity, secrets, data protection, and privileged access.
• Design MCP and model gateways, execution environments, skill registries, and enterprise integrations, with end-to-end observability, auditability, performance, availability, and disaster-recovery standards.

3. Agent, Skill, and MCP Ecosystem Design
• Define standards for reusable agents, skills, workflows, orchestration, context, memory, tool usage, delegation, and human oversight.
• Architect and, where appropriate, develop MCP servers connecting models securely to enterprise applications, data, developer tools, and fintech APIs; maintain an approved registry and certification process for agents, skills, prompts, tools, and MCP services.

4. Token Economics and Cost Governance
• Establish budgets, quotas, alerts, allocation or chargeback models, and controls for enterprise agentic workloads.
• Monitor token, model, and infrastructure cost by agent, team, and use case; optimize model selection, context, caching, routing, and API usage, and partner with Finance and Procurement to forecast spend and measure return on investment.

5. Agentic AI Center of Excellence and Enablement
• Lead architecture for the Agentic AI Center of Excellence and publish reference architectures, playbooks, standards, and reusable patterns.
• Build training, architecture forums, communities of practice, and certification programs; advise teams and support pilots and production adoption while tracking productivity, quality, cost, and risk outcomes.

6. Governance, Risk, and Production Assurance
• Define governance for agent ownership, accountability, approval, production access, and ongoing operation.
• Establish controls for data leakage, prompt injection, hallucination, model misuse, unauthorized actions, and excessive autonomy, including monitoring, audit trails, rollback, and incident response; partner with Security, Legal, Privacy, Risk, Compliance, and architecture review boards.

7. Value Measurement and Continuous Improvement
• Define measures for adoption, productivity, cycle time, code quality, reliability, risk, cost, and employee experience.
• Review outcomes, remediate or retire underperforming and high-risk use cases, and update the roadmap as models, tools, standards, and market capabilities evolve.

Requirements
• Significant enterprise, solution, or platform architecture experience, including standards, governance, design reviews, and technology roadmaps.
• Demonstrated experience designing and implementing enterprise AI, generative AI, or agentic AI platforms.
• Strong knowledge of large language models, agent orchestration, retrieval-augmented generation, tool calling, context management, and human-in-the-loop architectures.
• Hands-on experience with agentic development tools such as OpenAI Codex, Anthropic Claude Code, GitHub Copilot, and with MCP servers, gateways, or comparable integrations.
• Strong knowledge of identity and access management, API security, secrets, data protection, observability, cloud architecture, CI/CD, and secure software delivery.
• Understanding of AI risk, model governance, privacy, regulatory compliance, and controls in regulated environments, with the ability to communicate clearly to executive, technical, risk, and business stakeholders.

Leadership and Behavioral Expectations
• Enterprise-oriented and technically credible, balancing innovation with security, resilience, compliance, and cost discipline.
• Pragmatic and outcome-focused, comfortable moving from executive strategy to detailed architecture and implementation.
• An influential communicator who builds alignment across Product, Engineering, Security, Risk, Compliance, Finance, and business leadership.

Measures of Success
• Adoption of a ADLC with focus on Product and Engineering, approved agentic platform, and supporting architecture and governance standards.
• Increasing use and reuse of certified agents, skills, MCP servers, and reference architectures through approved platforms, supported by trained practitioners.
• Measurable gains in delivery speed, quality, reliability, productivity, and value.

Benefits

• Competitive holiday allowance
• 401K Matching program
• Group Insurance Benefits
• Flexible working model
• Employee Assistance Program

Working Language
English (written and spoken) is the language used most of the time, as work colleagues, clients, and strategic suppliers are geographically dispersed.

Our recruitment process may use automated tools, including AI, to support application management and candidate shortlisting.