Principal: AI & Product Engineer - Kenya

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.
    

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.