Senior Python Backend Engineer

LangGraph · Distributed Systems · Financial Transaction Platform
Full-time · Senior level

About the Role
We’re looking for a senior Python engineer to help evolve and scale a critical transaction
processing platform that handles millions of financial transactions every month. This is not a
CRUD application, a chatbot project, or an AI prototype.

You’ll be working on a distributed, event-driven system responsible for ingesting, processing,
classifying, enriching and synchronising financial transaction data across multiple services and
products. The platform sits at the centre of our ecosystem and communicates with systems
written in Python, PHP and TypeScript.

The role requires someone who is equally comfortable designing distributed systems, building
production-grade APIs, optimising LLM-powered workflows, and understanding the
consequences of architectural decisions across multiple interconnected services. Experience
with LangGraph, AWS SQS, SQL and LLM optimisation is mandatory.

A significant portion of the platform relies on graph-based workflows for transaction
classification, orchestration, enrichment and decision-making. We are looking for engineers who
have built and operated LangGraph systems in production, understand event-driven
architectures at scale, and know how to optimise LLM usage through intelligent caching,
batching, routing and cost controls. Every change has a ripple effect across queues, APIs,
databases, classification pipelines, tax calculations and customer-facing products. Success in
this role requires strong systems thinking, attention to detail, and the ability to anticipate
downstream impacts before they become production incidents.

The Platform
Our transaction processing platform is responsible for:

• Processing millions of financial transactions
• Parsing structured and unstructured financial data
• Classifying transactions using LLM-powered workflows
• Extracting and enriching financial information
• Synchronising data across multiple internal services
• Feeding downstream bookkeeping, tax and reporting systems

Operating asynchronously through distributed worker architectures
The platform combines traditional backend engineering, distributed systems design and modern
AI orchestration techniques.

Technology Stack

• Python 3.13 FastAPI SQLAlchemy
• MySQL LangGraph AWS SQS
• Event-driven workers
• Asynchronous processing
• pipelines
• Pandas
• PDF processing
• LangFuse
• Pytest

What You’ll Be Doing

• Designing and implementing new transaction processing features
• Building and maintaining LangGraph workflows
• Improving transaction classification accuracy and reliability
• Designing robust event-driven processing pipelines
• Optimising LLM utilisation, latency and cost
• Implementing intelligent caching and routing strategies
• Building APIs consumed by PHP and TypeScript applications
• Designing integrations between multiple internal services
• Contributing to architectural decisions across the platform
• Improving throughput, reliability and operational efficiency

This role involves significant ownership and influence over technical direction.
Example Problems You Might Solve
1. Designing a classification workflow capable of processing hundreds of thousands of
transactions daily without creating bottlenecks.
2. Building cache strategies that reduce LLM costs by 70% while maintaining classification
accuracy.
3. Designing idempotent processing pipelines that safely recover from worker failures and
duplicate events.
4. Tracing inconsistencies across Python, PHP and TypeScript systems to identify the root
cause of financial data discrepancies.
5. Designing queue architectures that remain performant as transaction volumes increase
significantly.
6. Identifying and mitigating subtle downstream impacts of schema, workflow or classification
changes before deployment.

Mandatory Experience !!!
Please do not apply unless you have experience with all of the following !!!:
 

• Strong Python backend engineering experience
• Production experience with LangGraph
• AWS SQS and event-driven architectures
• SQL database design and optimisation
• FastAPI or equivalent API frameworks
• Designing and operating distributed systems
• LLM optimisation techniques
• Production debugging and observability
• Automated testing using Pytest or equivalent

Who This Role Suits
This role is best suited to engineers who enjoy complexity, care deeply about system design,
and want to build highly reliable backend platforms operating at significant scale.