Role Overview:

We are hiring a Principal: AI & Product Engineer to lead the development and deployment of AI-driven product experiences across our B2B payments platform, with an initial focus on reconciliation and merchant onboarding workflows. This role is responsible for building production-grade AI features leveraging LLMs, embeddings, agent orchestration frameworks, and secure cloud-native architectures.

The role begins as a hands-on Principal IC focused on delivering AI features, and evolves into the founding engineer and lead for our internal AI Platform, including prompt gateways, vector search infrastructure, agent orchestration, and reusable AI APIs for all product squads.

This is a high-impact role with aggressive timelines—PoC delivery in 45 days and first production AI feature at day 90. The ideal candidate blends deep enterprise engineering experience, cloud native development expertise with strong AI systems intuition and a solid understanding of fintech/payments workflows.

What You'll do:

Deliver AI Features for Reconciliation & Onboarding (Phase 1 Priority)

• Build semi-autonomous AI agents to automate reconciliation workflows, including:
  • Payment method and bank reports/ statement ingestion
  • Transaction matching
  • Discrepancy analysis
  • Exception explanation and routing
  • Report generation
• Develop AI-assisted KYB/KYC extraction tools to accelerate onboarding:
  • Document parsing (IDs, certificates, statements).
  • Entity extraction & validation.
  • Risk flag identification.
• Build necessary API interfaces and Integrate AI services into existing/new microservices and event-driven pipelines.

AI Engineering, LLM Integration & Agent Orchestration

• Integrate with multiple LLM providers through a hybrid model strategy (commercial APIs + open-source models).
• Implement prompt engineering, safety guardrails, and mechanisms to mitigate hallucinations during workflow execution.
• Build and integrate semi-autonomous agents using LangGraph or similar frameworks.
• Design high-quality APIs, SDKs, and internal tooling to allow product squads to embed AI seamlessly.
• Work with vector databases (PGVector, Pinecone, Weaviate—nice to have) for retrieval augmentation, semantic search, and agent memory.

Cloud-Native & Enterprise Engineering Responsibilities

• Deploy cloud-native AI services on AWS using Kubernetes, Docker, CI/CD pipelines, and secure infra patterns.
• Build scalable backend services using Spring Boot and event-driven flows via Kafka/RabbitMQ.
• Implement observability for AI systems (tracing, cost monitoring, latency, and prompt logs).
• Ensure strict compliance with:
  • PCI DSS (tokenization boundaries, card-data safety).
  • GDPR / data privacy
  • Sensitive document handling for KYC/KYB and bank/payment method statements.
  • Auditability and traceability for all AI outputs
  • Model governance & safe operations

Cross-Functional Collaboration & Product Influence

• Partner with Product, Data Engineering, Finance Ops, Risk Ops, and Compliance to automate high-impact workflows.
• Translate complex business processes into AI-driven workflows with clear, measurable outcomes.
• Partner with Engineering and Platform teams to design, evolve and build out our next-gen payment architecture ensuring scalability, and AI integration ready design from the get go.
• Contribute (but not own) data ingestion pipelines needed for AI agents (PDF/CSV parsing, structured extraction e.t.c).

AI Platform Evolution (Phase 2 Priority)

After demonstrating initial business value:

• Design and lead the build-out of our internal AI Platform, including:
  • AI gateway for model routing
  • Prompt library & prompt evaluation tooling
  • Retrieval pipelines & vector stores
  • Agent orchestration frameworks
  • Enterprise-grade governance and safety controls.
• Act as the founding member of a future AI Product Engineering team, likely taking on the technical leadership role of the team as the platform expands.
• Educate and coach internal squads on safe and effective use of AI tools.

What we are looking for:

Minimum Qualifications (Required)

AI & LLM Engineering

• Strong experience integrating LLMs into production systems.
• Hands-on prompt engineering, guardrails, and hallucination mitigation experience.
• Experience building cloud-native AI services.

Enterprise Backend Engineering

• 8+ years as a senior/principal engineer building large-scale enterprise systems.
• Deep experience with:
  • Java/Spring Boot
  • REST APIs & microservices
  • Kafka or RabbitMQ
  • AWS + Kubernetes + Docker
  • Postgres or MySQL
  • Redis + Elastic

Fintech /Payments Expertise (Required)

• Experience with:
  • Deep understanding of the end to end payments processing workflows.
  • Reconciliation flows.
  • Merchant onboarding & KYB/KYC.
  • Settlement & payouts.
  • Exception handling.
  • Payment methods across multiple channels

Security, Governance & Compliance

• Understanding of:
  • PCI DSS boundaries
  • GDPR & data privacy
  • Audit logging & traceability
  • Sensitive document handling.

Preferred Qualifications (Nice to Have)

• LangGraph experience (agent orchestration)
• LangChain / RAG systems
• Vector DB experience (PGVector, Pinecone, Weaviate)
• Multi-agent orchestration
• Model fine-tuning or retrieval-augmented fine-tuning
• Python experience for AI workflows
• Experience scaling AI systems in production environments

• Build semi-autonomous AI agents to automate reconciliation workflows, including: Payment method and bank reports/ statement ingestion, Transaction matching, Discrepancy analysis, Exception explanation and routing, Report generation
• Develop AI-assisted KYB/KYC extraction tools to accelerate onboarding: Document parsing (IDs, certificates, statements), Entity extraction & validation, Risk flag identification
• Build necessary API interfaces and Integrate AI services into existing/new microservices and event-driven pipelines
• Integrate with multiple LLM providers through a hybrid model strategy (commercial APIs + open-source models)
• Implement prompt engineering, safety guardrails, and mechanisms to mitigate hallucinations during workflow execution
• Build and integrate semi-autonomous agents using LangGraph or similar frameworks
• Design high-quality APIs, SDKs, and internal tooling to allow product squads to embed AI seamlessly
• Work with vector databases (PGVector, Pinecone, Weaviate—nice to have) for retrieval augmentation, semantic search, and agent memory
• Deploy cloud-native AI services on AWS using Kubernetes, Docker, CI/CD pipelines, and secure infra patterns
• Build scalable backend services using Spring Boot and event-driven flows via Kafka/RabbitMQ
• Implement observability for AI systems (tracing, cost monitoring, latency, and prompt logs)
• Ensure strict compliance with: PCI DSS (tokenization boundaries, card-data safety), GDPR / data privacy, Sensitive document handling for KYC/KYB and bank/payment method statements, Auditability and traceability for all AI outputs, Model governance & safe operations
• Partner with Product, Data Engineering, Finance Ops, Risk Ops, and Compliance to automate high-impact workflows
• Translate complex business processes into AI-driven workflows with clear, measurable outcomes
• Partner with Engineering and Platform teams to design, evolve and build out our next-gen payment architecture ensuring scalability, and AI integration ready design from the get go
• Contribute (but not own) data ingestion pipelines needed for AI agents (PDF/CSV parsing, structured extraction e.t.c)
• Design and lead the build-out of our internal AI Platform, including: AI gateway for model routing, Prompt library & prompt evaluation tooling, Retrieval pipelines & vector stores, Agent orchestration frameworks, Enterprise-grade governance and safety controls
• Act as the founding member of a future AI Product Engineering team, likely taking on the technical leadership role of the team as the platform expands
• Educate and coach internal squads on safe and effective use of AI tools

• Strong experience integrating LLMs into production systems
• Hands-on prompt engineering, guardrails, and hallucination mitigation experience
• Experience building cloud-native AI services
• Deep experience with Java/Spring Boot
• REST APIs & microservices
• Kafka or RabbitMQ
• AWS + Kubernetes + Docker
• Postgres or MySQL
• Redis + Elastic
• Deep understanding of the end to end payments processing workflows
• Reconciliation flows
• Merchant onboarding & KYB/KYC
• Settlement & payouts
• Exception handling
• Payment methods across multiple channels
• Understanding of PCI DSS boundaries
• GDPR & data privacy
• Audit logging & traceability
• Sensitive document handling
• LangGraph experience (agent orchestration)
• LangChain / RAG systems
• Vector DB experience (PGVector, Pinecone, Weaviate)
• Multi-agent orchestration
• Model fine-tuning or retrieval-augmented fine-tuning
• Python experience for AI workflows
• Experience scaling AI systems in production environments

• BA/BSc/HND
• 8 years of experience
• Strong experience integrating LLMs into production systems
• Hands-on prompt engineering, guardrails, and hallucination mitigation experience
• Experience building cloud-native AI services
• 8+ years as a senior/principal engineer building large-scale enterprise systems
• Deep experience with Java/Spring Boot, REST APIs & microservices, Kafka or RabbitMQ, AWS + Kubernetes + Docker, Postgres or MySQL, Redis + Elastic
• Experience with deep understanding of the end to end payments processing workflows, Reconciliation flows, Merchant onboarding & KYB/KYC, Settlement & payouts, Exception handling, Payment methods across multiple channels
• Understanding of PCI DSS boundaries, GDPR & data privacy, Audit logging & traceability, Sensitive document handling