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.