AI Automation Engineer - Payments at Datonomy Solutions

The bank is building a next-generation AI Payments Engineering capability to design and deliver an AI-native Payments Operating System. The AI Automation Engineer will combine AI engineering, systems integration and production automation to transform high-effort, high-risk Payments processes into scalable, intelligent and auditable workflows. The objective is efficiency with control integrity, not automation speed alone.

Core Focus Design, build, test, deploy and maintain production-grade AI automation workflows that deliver measurable operational impact across Payments.

Illustrative Use Cases

• Payments reconciliations and exception handling.
• Audit and reporting preparation.
• Financial analytics and operational insight.
• Incident detection, triage and response automation.
• Least-cost routing and related Payments decision processes.
• Agentic workflow engineering:  Design and deploy multi-step AI workflows using LLM orchestration, tools, agents and workflow engines.
• Software engineering:  Build orchestration services using Java with Spring AI and/or Python-based frameworks.
• Model integration:  Integrate Claude or Anthropic APIs and comparable enterprise AI services.
• Workflow orchestration:  Use n8n or equivalent engines to coordinate reliable, supportable automation.
• Agent and tool integration:  Use MCP or comparable context, tool and agent-coordination approaches where appropriate.
• Systems integration:  Connect internal and external services through APIs, data pipelines and reporting systems.
• Automation discovery:  Identify high-value opportunities to reduce manual effort across Payments operations.
• Production ownership:  Test, deploy, monitor, maintain and optimise solutions and supporting platform components.
• Human oversight:  Design human approvals and intervention points for high-risk or judgement-dependent activities.
• Classify proposed automation by risk tier before development.
• Ensure every solution has a named Process Owner and documented data flows.
• Implement appropriate access controls, audit logging and traceability.
• Understand and mitigate LLM and agent failure modes in high-stakes environments.
• Preserve control integrity, compliance and auditability throughout the automation lifecycle.
• Develop reusable frameworks, templates, components and workflow patterns.
• Create engineering documentation and standards that support adoption at scale.
• Build dashboards and metrics covering efficiency gains, cost reduction, risk outcomes and return on investment.
• Train Payments teams on AI-enabled workflows and tools.
• Hands-on experience deploying production-grade AI, GenAI or agentic systems.
• Strong understanding of LLM orchestration, agentic workflows, multi-agent systems and AI failure modes.
• Proficiency in Java with Spring AI and/or Python-based orchestration.
• Experience with n8n or an equivalent workflow engine.
• Experience integrating Anthropic/Claude or comparable AI APIs.
• Strong API and systems-integration capability across backend, data and reporting platforms.
• Ability to deliver across discovery, design, build, deployment and optimisation.
• Strong architectural thinking for scalable, reusable and future-ready platforms.
• Ability to bridge Payments, Finance, Compliance and Engineering requirements.
• Collaborate with Payments backend, infrastructure, platform, Compliance, Audit and AI Centre of Excellence teams.
• Translate business and finance requirements into technical solutions and roadmaps.
• Communicate effectively with technical and non-technical stakeholders.
• Operate successfully in fast-paced, ambiguous environments with an ownership mindset and bias for execution.

Key Responsibilities AI Governance and Risk Management Platform and Capability Building Required Experience and Expertise Stakeholder and Ways of Working

Desired Skills

• AI Automation Engineer
• AI Engineer
• Generative AI Engineer