Senior AI Engineer (Payments Architecture & Strategy)

AI Payments Engineering Team – Job Description
Roles:
•    AIOps Engineer
•    AI Automation Engineer
•    Senior AI Engineer (Payments & Architecture)

Team Overview
We are building a next-generation AI Payments Engineering Team to design and deliver an AI-native Payments Operating System.
This team will combine AI engineering, payments domain expertise, and production automation to transform high-effort, high-risk financial operations into scalable, intelligent, and auditable systems.
The team will focus on automating critical Payments processes such as Least cost routing, reconciliations, audit, Incident automation etc.
The mission is to deliver efficiency with control integrity—not just automation speed.

3. Senior AI Engineer (Payments Architecture & Strategy)
Core Focus
Own the long-term architecture and evolution toward an AI-native Payments platform.
Key Responsibilities
•    Assess and map end-to-end Payments operations, including:
o    Reconciliations
o    Reporting & controls
o    Audit

Identify

• Operational bottlenecks
  • Capability gaps
  • Integration failures
• Define a Payments automation roadmap based on:
  • Operational impact
  • Regulatory risk
  • Scalability
• Design the AI-native Payments Operating System, including:
  • Agentic system design
  • Data pipelines
  • Integration with financial systems
  • Control and reporting frameworks
• Design and implement an agentic Payments layer that:
  • Operates alongside the current stack
  • Automates workflows today
  • Enables gradual migration to a future-state architecture
• Evaluate and simplify the existing Payments technology stack
• Represent Payments in the AI Center of Excellence, ensuring alignment across teams

 

AI Governance & Risk Management

• Classify all automation by risk tier before development
• Ensure each solution includes:
  • Named Process Owner
  • Documented data flows
  • Access controls
  • Full audit logging
• Design automation that preserves control integrity in regulated environments

 

Platform & Capability Building

• Develop reusable frameworks, templates, and workflows
• Create documentation and standards to scale adoption
• Build dashboards and metrics to measure:
  • Efficiency gains
  • Cost reduction
  • Risk outcomes
  • ROI
• Train Payments teams on AI-enabled workflows and tools

 

Collaboration & Stakeholder Engagement

• Work closely with:
  • Payments Backend teams
  • Infrastructure & platform teams
  • Compliance & audit teams
  • AI Center of Excellence
• Translate business and finance requirements into technical solutions and roadmaps
• Communicate effectively with technical and non-technical stakeholders

 

What the Team Brings

Technical Expertise

• Hands-on experience deploying production-grade AI/agentic systems
• Strong understanding of:
  • LLM orchestration
  • Multi-agent systems
  • AI failure modes in high-stakes environments
• Proficiency in:
  • Spring AI/Python-based orchestration
  • Workflow engines (n8n or equivalent)
  • AI APIs (Anthropic/Claude or similar)

 

Systems & Architecture Thinking

• Ability to deliver across the lifecycle: Discovery → Design → Build → Deploy → Optimize
• Strong architectural thinking for scalable, future-ready platforms

 

Ways of Working

• Operates effectively in fast-paced, ambiguous environments
• Strong ownership mindset with a bias for execution
• Ability to bridge business and engineering domains

AI Payments Engineering Team – Job Description
Roles:
•    AIOps Engineer
•    AI Automation Engineer
•    Senior AI Engineer (Payments & Architecture)

Team Overview
We are building a next-generation AI Payments Engineering Team to design and deliver an AI-native Payments Operating System.
This team will combine AI engineering, payments domain expertise, and production automation to transform high-effort, high-risk financial operations into scalable, intelligent, and auditable systems.
The team will focus on automating critical Payments processes such as Least cost routing, reconciliations, audit, Incident automation etc.
The mission is to deliver efficiency with control integrity—not just automation speed.

3. Senior AI Engineer (Payments Architecture & Strategy)
Core Focus
Own the long-term architecture and evolution toward an AI-native Payments platform.
Key Responsibilities
•    Assess and map end-to-end Payments operations, including:
o    Reconciliations
o    Reporting & controls
o    Audit

Identify

• Operational bottlenecks
  • Capability gaps
  • Integration failures
• Define a Payments automation roadmap based on:
  • Operational impact
  • Regulatory risk
  • Scalability
• Design the AI-native Payments Operating System, including:
  • Agentic system design
  • Data pipelines
  • Integration with financial systems
  • Control and reporting frameworks
• Design and implement an agentic Payments layer that:
  • Operates alongside the current stack
  • Automates workflows today
  • Enables gradual migration to a future-state architecture
• Evaluate and simplify the existing Payments technology stack
• Represent Payments in the AI Center of Excellence, ensuring alignment across teams

 

AI Governance & Risk Management

• Classify all automation by risk tier before development
• Ensure each solution includes:
  • Named Process Owner
  • Documented data flows
  • Access controls
  • Full audit logging
• Design automation that preserves control integrity in regulated environments

 

Platform & Capability Building

• Develop reusable frameworks, templates, and workflows
• Create documentation and standards to scale adoption
• Build dashboards and metrics to measure:
  • Efficiency gains
  • Cost reduction
  • Risk outcomes
  • ROI
• Train Payments teams on AI-enabled workflows and tools

 

Collaboration & Stakeholder Engagement

• Work closely with:
  • Payments Backend teams
  • Infrastructure & platform teams
  • Compliance & audit teams
  • AI Center of Excellence
• Translate business and finance requirements into technical solutions and roadmaps
• Communicate effectively with technical and non-technical stakeholders

 

What the Team Brings

Technical Expertise

• Hands-on experience deploying production-grade AI/agentic systems
• Strong understanding of:
  • LLM orchestration
  • Multi-agent systems
  • AI failure modes in high-stakes environments
• Proficiency in:
  • Spring AI/Python-based orchestration
  • Workflow engines (n8n or equivalent)
  • AI APIs (Anthropic/Claude or similar)

 

Systems & Architecture Thinking

• Ability to deliver across the lifecycle: Discovery → Design → Build → Deploy → Optimize
• Strong architectural thinking for scalable, future-ready platforms

 

Ways of Working

• Operates effectively in fast-paced, ambiguous environments
• Strong ownership mindset with a bias for execution
• Ability to bridge business and engineering domains

AI Payments Engineering Team – Job Description
Roles:
•    AIOps Engineer
•    AI Automation Engineer
•    Senior AI Engineer (Payments & Architecture)

Team Overview
We are building a next-generation AI Payments Engineering Team to design and deliver an AI-native Payments Operating System.
This team will combine AI engineering, payments domain expertise, and production automation to transform high-effort, high-risk financial operations into scalable, intelligent, and auditable systems.
The team will focus on automating critical Payments processes such as Least cost routing, reconciliations, audit, Incident automation etc.
The mission is to deliver efficiency with control integrity—not just automation speed.

3. Senior AI Engineer (Payments Architecture & Strategy)
Core Focus
Own the long-term architecture and evolution toward an AI-native Payments platform.
Key Responsibilities
•    Assess and map end-to-end Payments operations, including:
o    Reconciliations
o    Reporting & controls
o    Audit

Identify

• Operational bottlenecks
  • Capability gaps
  • Integration failures
• Define a Payments automation roadmap based on:
  • Operational impact
  • Regulatory risk
  • Scalability
• Design the AI-native Payments Operating System, including:
  • Agentic system design
  • Data pipelines
  • Integration with financial systems
  • Control and reporting frameworks
• Design and implement an agentic Payments layer that:
  • Operates alongside the current stack
  • Automates workflows today
  • Enables gradual migration to a future-state architecture
• Evaluate and simplify the existing Payments technology stack
• Represent Payments in the AI Center of Excellence, ensuring alignment across teams

 

AI Governance & Risk Management

• Classify all automation by risk tier before development
• Ensure each solution includes:
  • Named Process Owner
  • Documented data flows
  • Access controls
  • Full audit logging
• Design automation that preserves control integrity in regulated environments

 

Platform & Capability Building

• Develop reusable frameworks, templates, and workflows
• Create documentation and standards to scale adoption
• Build dashboards and metrics to measure:
  • Efficiency gains
  • Cost reduction
  • Risk outcomes
  • ROI
• Train Payments teams on AI-enabled workflows and tools

 

Collaboration & Stakeholder Engagement

• Work closely with:
  • Payments Backend teams
  • Infrastructure & platform teams
  • Compliance & audit teams
  • AI Center of Excellence
• Translate business and finance requirements into technical solutions and roadmaps
• Communicate effectively with technical and non-technical stakeholders

 

What the Team Brings

Technical Expertise

• Hands-on experience deploying production-grade AI/agentic systems
• Strong understanding of:
  • LLM orchestration
  • Multi-agent systems
  • AI failure modes in high-stakes environments
• Proficiency in:
  • Spring AI/Python-based orchestration
  • Workflow engines (n8n or equivalent)
  • AI APIs (Anthropic/Claude or similar)

 

Systems & Architecture Thinking

• Ability to deliver across the lifecycle: Discovery → Design → Build → Deploy → Optimize
• Strong architectural thinking for scalable, future-ready platforms

 

Ways of Working

• Operates effectively in fast-paced, ambiguous environments
• Strong ownership mindset with a bias for execution
• Ability to bridge business and engineering domains