The Role
Lead design and roadmap for an AI-native Payments Operating System: assess end-to-end payments operations, identify automation opportunities, design agentic architecture, data pipelines, model access, integrations, governance, controls, and observability; run proofs-of-value, define standards, align with enterprise AI and security, and provide technical leadership and stakeholder communication.
Summary Generated by Built In
Position Summary
We are seeking a Senior AI Engineer / AI Solution Architect to own the architecture and evolution of an AI-native Payments Operating System. This role will map end-to-end payment operations, identify automation opportunities and capability gaps, define the target architecture, and establish a pragmatic roadmap that delivers near-term value while enabling long-term platform modernization.
The role requires a strong combination of Payments domain knowledge, enterprise architecture, AI platform design, integration strategy, governance, and stakeholder leadership.
Key Responsibilities
Assess end-to-end Payments operations, including reconciliation, routing, settlement, reporting, controls, audit, exceptions, and incident management.
Identify manual effort, operational bottlenecks, control weaknesses, integration failures, data gaps, and architecture complexity.
Define a prioritized Payments AI and automation roadmap based on business value, risk, feasibility, scalability, and ROI.
Design the target AI-native Payments Operating System, including agent architecture, orchestration, data pipelines, model access, integration, controls, reporting, and observability.
Design an agentic Payments layer that operates alongside the current technology stack and supports gradual migration toward a future-state platform.
Define patterns for AI gateways, model selection, prompt and agent registries, RAG/knowledge services, workflow engines, human approval, and auditability.
Evaluate and simplify the existing Payments technology landscape, reducing duplication and improving interoperability.
Ensure architecture incorporates security, data governance, model risk, explainability, resilience, privacy, and regulatory controls by design.
Lead architecture reviews, proof-of-value initiatives, technical standards, reusable frameworks, and platform capability development.
Represent Payments within the AI Center of Excellence and align with enterprise AI, data, cloud, security, and governance strategies.
Communicate architecture, investment choices, risks, and roadmaps to engineering leaders, business stakeholders, risk, compliance, and executives.
Mentor engineers and provide technical leadership from discovery through production optimization.
Required Experience
10+ years of software engineering, solution architecture, enterprise architecture, or platform engineering experience.
At least 3 years designing Generative AI, LLM, machine-learning, or intelligent automation solutions.
Demonstrated experience leading architecture for enterprise-scale transformation programs.
Strong Payments or Financial Services experience, ideally involving payment processing, reconciliation, settlement, ledgers, routing, or operational controls.
Experience defining target-state architecture, transition architecture, technology roadmaps, and implementation governance.
Experience engaging senior business, engineering, risk, compliance, audit, and executive stakeholders.
Mandatory Technical and Architecture Skills
AI architecture: LLM platforms, multi-agent systems, tool calling, RAG, embeddings, vector stores, model gateways, evaluation, and guardrails.
Agent orchestration: Spring AI, LangGraph, LangChain, MCP, Semantic Kernel, AutoGen, or comparable frameworks.
Enterprise architecture: microservices, event-driven architecture, API strategy, integration platforms, domain-driven design, and distributed systems.
Payments architecture: transaction flows, reconciliation, settlement, payment routing, ledger interaction, exception handling, and audit controls.
Platform architecture: Kubernetes, cloud platforms, Kafka/event streaming, API gateways, CI/CD, observability, security, IAM, and data pipelines.
Governance: risk tiering, data classification, model risk, access control, traceability, explainability, human oversight, and audit logging.
Roadmapping: value assessment, architectural trade-offs, dependency management, phased migration, cost modeling, and ROI measurement.
Preferred / Nice-to-Have Skills
ISO 20022, SWIFT, ACH, real-time payments, card networks, merchant acquiring, payment gateways, or treasury operations.
PCI DSS, SOC 2, model risk management, responsible AI, privacy regulations, and banking control frameworks.
TOGAF, cloud architecture, security, or payments-related certifications.
Experience establishing AI Centers of Excellence, engineering standards, reference architectures, or reusable platform services.
Experience balancing legacy modernization with incremental delivery and measurable business outcomes.
We are seeking a Senior AI Engineer / AI Solution Architect to own the architecture and evolution of an AI-native Payments Operating System. This role will map end-to-end payment operations, identify automation opportunities and capability gaps, define the target architecture, and establish a pragmatic roadmap that delivers near-term value while enabling long-term platform modernization.
The role requires a strong combination of Payments domain knowledge, enterprise architecture, AI platform design, integration strategy, governance, and stakeholder leadership.
Key Responsibilities
Assess end-to-end Payments operations, including reconciliation, routing, settlement, reporting, controls, audit, exceptions, and incident management.
Identify manual effort, operational bottlenecks, control weaknesses, integration failures, data gaps, and architecture complexity.
Define a prioritized Payments AI and automation roadmap based on business value, risk, feasibility, scalability, and ROI.
Design the target AI-native Payments Operating System, including agent architecture, orchestration, data pipelines, model access, integration, controls, reporting, and observability.
Design an agentic Payments layer that operates alongside the current technology stack and supports gradual migration toward a future-state platform.
Define patterns for AI gateways, model selection, prompt and agent registries, RAG/knowledge services, workflow engines, human approval, and auditability.
Evaluate and simplify the existing Payments technology landscape, reducing duplication and improving interoperability.
Ensure architecture incorporates security, data governance, model risk, explainability, resilience, privacy, and regulatory controls by design.
Lead architecture reviews, proof-of-value initiatives, technical standards, reusable frameworks, and platform capability development.
Represent Payments within the AI Center of Excellence and align with enterprise AI, data, cloud, security, and governance strategies.
Communicate architecture, investment choices, risks, and roadmaps to engineering leaders, business stakeholders, risk, compliance, and executives.
Mentor engineers and provide technical leadership from discovery through production optimization.
Required Experience
10+ years of software engineering, solution architecture, enterprise architecture, or platform engineering experience.
At least 3 years designing Generative AI, LLM, machine-learning, or intelligent automation solutions.
Demonstrated experience leading architecture for enterprise-scale transformation programs.
Strong Payments or Financial Services experience, ideally involving payment processing, reconciliation, settlement, ledgers, routing, or operational controls.
Experience defining target-state architecture, transition architecture, technology roadmaps, and implementation governance.
Experience engaging senior business, engineering, risk, compliance, audit, and executive stakeholders.
Mandatory Technical and Architecture Skills
AI architecture: LLM platforms, multi-agent systems, tool calling, RAG, embeddings, vector stores, model gateways, evaluation, and guardrails.
Agent orchestration: Spring AI, LangGraph, LangChain, MCP, Semantic Kernel, AutoGen, or comparable frameworks.
Enterprise architecture: microservices, event-driven architecture, API strategy, integration platforms, domain-driven design, and distributed systems.
Payments architecture: transaction flows, reconciliation, settlement, payment routing, ledger interaction, exception handling, and audit controls.
Platform architecture: Kubernetes, cloud platforms, Kafka/event streaming, API gateways, CI/CD, observability, security, IAM, and data pipelines.
Governance: risk tiering, data classification, model risk, access control, traceability, explainability, human oversight, and audit logging.
Roadmapping: value assessment, architectural trade-offs, dependency management, phased migration, cost modeling, and ROI measurement.
Preferred / Nice-to-Have Skills
ISO 20022, SWIFT, ACH, real-time payments, card networks, merchant acquiring, payment gateways, or treasury operations.
PCI DSS, SOC 2, model risk management, responsible AI, privacy regulations, and banking control frameworks.
TOGAF, cloud architecture, security, or payments-related certifications.
Experience establishing AI Centers of Excellence, engineering standards, reference architectures, or reusable platform services.
Experience balancing legacy modernization with incremental delivery and measurable business outcomes.
Skills Required
- 10+ years of software engineering, solution architecture, enterprise architecture, or platform engineering experience.
- At least 3 years designing Generative AI, LLM, machine-learning, or intelligent automation solutions.
- Demonstrated experience leading architecture for enterprise-scale transformation programs.
- Strong Payments or Financial Services experience (payment processing, reconciliation, settlement, routing, ledgers, operational controls).
- Experience defining target-state architecture, transition architecture, technology roadmaps, and implementation governance.
- Experience engaging senior business, engineering, risk, compliance, audit, and executive stakeholders.
- AI architecture skills: LLM platforms, multi-agent systems, tool calling, RAG, embeddings, vector stores, model gateways, evaluation, and guardrails.
- Agent orchestration frameworks: Spring AI, LangGraph, LangChain, MCP, Semantic Kernel, AutoGen, or comparable frameworks.
- Enterprise architecture skills: microservices, event-driven architecture, API strategy, integration platforms, domain-driven design, and distributed systems.
- Platform architecture skills: Kubernetes, cloud platforms, Kafka/event streaming, API gateways, CI/CD, observability, security, IAM, and data pipelines.
- Governance skills: risk tiering, data classification, model risk, access control, traceability, explainability, human oversight, and audit logging.
- Roadmapping skills: value assessment, architectural trade-offs, dependency management, phased migration, cost modeling, and ROI measurement.
- Knowledge of ISO 20022, SWIFT, ACH, real-time payments, card networks, merchant acquiring, payment gateways, or treasury operations.
- Experience with PCI DSS, SOC 2, model risk management, responsible AI, privacy regulations, or banking control frameworks.
- TOGAF, cloud architecture, security, or payments-related certifications.
- Experience establishing AI Centers of Excellence, engineering standards, reference architectures, or reusable platform services.
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The Company
What We Do
Icreon is a digital innovation partner for growth brands, helping businesses achieve exponential growth and operational efficiency.
