About 9fin
9fin is the AI platform powering global debt markets — the world’s largest asset class at over $145 trillion.
Debt markets are vast, global, and mission-critical, yet still run on fragmented data, PDFs, and manual workflows. 9fin replaces this broken infrastructure with a single platform that centralises proprietary credit data, deep analysis, and high-value workflows across global markets.
Today, 9fin powers teams at 300+ blue-chip institutions worldwide, including global banks, asset managers, private equity firms, law firms, and advisors. The business is scaling at exceptional speed, with rapid expansion in the US and best-in-class retention driven by deep workflow adoption.
We’re at a defining inflection point. With proven product-market fit and strong, global market pull, 9fin is accelerating toward becoming the category-defining platform for debt markets worldwide.
The AI Engineering team at 9fin is building the next generation of AI capabilities that will power our products for years to come. We're looking for a Senior AI Platform Engineer to help design, build, and evolve the core AI platform that enables Generative AI, Agentic AI, and machine learning applications across the business.
This is an opportunity to join at the beginning of the platform's journey. Rather than inheriting a mature internal platform, you'll help shape its architecture, define engineering standards, and build reusable infrastructure that allows AI teams to move quickly, safely, and at scale.
Working alongside backend engineers and AI engineers, you'll create the shared platform services that power everything from intelligent document understanding to autonomous AI agents capable of running scheduled workflows, generating morning briefings, identifying distressed companies, and supporting future AI-powered product experiences.
Every day is different, but here's an example of the kind of things you'll work on:
Design, build and continuously evolve the enterprise AI platform that powers AI products across 9fin.
Develop scalable AI services supporting Agentic AI, Retrieval-Augmented Generation (RAG), embeddings, vector search, model serving, and intelligent automation.
Build orchestration layers that transform structured and unstructured enterprise data into reusable AI capabilities.
Develop production-ready AI applications using modern LLM frameworks and orchestration tools.
Design reusable platform components for prompt management, model serving, vector search, embeddings, AI gateways, and evaluation services.
Build APIs, SDKs, and developer tooling that enable self-service AI development across engineering teams.
Design secure, scalable deployment pipelines for AI models and applications.
Build AI observability capabilities including monitoring, tracing, evaluation, cost optimisation, and production quality measurement.
Collaborate closely with AI Engineers, Backend Engineers and Engineering Leadership to define platform architecture and engineering standards.
Establish engineering best practices around testing, governance, Responsible AI, deployment, and operational excellence.
Continuously evaluate emerging AI technologies and evolve the platform as the ecosystem rapidly advances.
Unlike many AI Platform roles that focus on maintaining existing infrastructure, this role is centred around building the platform from the ground up.
You'll join at an early stage where many architectural decisions have yet to be made, giving you genuine influence over how AI is built, deployed, and operated across 9fin.
Within your first six months, you'll help build the platform that powers production AI agents capable of running autonomously in the background—supporting workflows such as scheduled market briefings, distressed company discovery, and future intelligent financial research products.
Rather than owning a single AI product, you'll build the shared capabilities that enable multiple engineering teams to rapidly develop, deploy, and operate AI solutions at scale.
You'll be one of a small number of senior engineers helping shape the technical direction of the platform alongside the Engineering Lead and fellow senior engineers.
This role will be a great fit if you:
Have 5+ years of software engineering experience, including 2+ years building AI/ML platforms, Generative AI applications, or production machine learning systems.
Have experience designing and deploying enterprise AI applications into production with a strong focus on scalability, reliability, and developer experience.
Have hands-on experience building applications powered by Large Language Models (LLMs).
Have experience building Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG) solutions, making use of embeddings, vector databases, and modern LLM orchestration frameworks.
Have experience building or contributing to agentic AI systems, intelligent workflows, or orchestration frameworks.
Are a strong backend engineer with production experience using Python and/or TypeScript.
Have designed scalable backend services, REST APIs, and event-driven architectures.
Have experience building reusable platform capabilities, internal developer tooling, SDKs, or shared engineering services.
Have strong experience building cloud-native applications.
Are comfortable working with Docker, Kubernetes, CI/CD pipelines, and containerised deployments.
Have experience with solutions such as AWS Bedrock and AgentCore
Understand how to deploy, monitor, and operate AI services in production.
Have experience implementing monitoring, tracing, evaluation, and cost optimisation for AI systems.
Have experience with observability solutions such as Arize Phoenix, Langfuse, or Langsmith
Understand the operational challenges of deploying LLM-powered applications, including latency, reliability, hallucination monitoring, and model quality evaluation.
Enjoy collaborating across multiple engineering disciplines to shape technical direction and establish engineering best practices.
Are comfortable operating as one of a small number of senior engineers, influencing architecture and platform direction without necessarily having formal management responsibilities.
Thrive in fast-moving environments where many platform capabilities are still being designed and built.
Experience with any of the following would be highly advantageous:
AI gateways and model routing
Prompt management platforms
AI evaluation frameworks
LangGraph, LangChain, LlamaIndex, DSPy, CrewAI or similar orchestration frameworks
Vector databases and semantic retrieval
Knowledge graphs or document understanding systems
Building internal AI platforms used by multiple engineering teams
We’re a scaling start up, and we enjoy sharing our success, when the company succeeds, we always reinvest that in our people. We also offer huge amounts of responsibility, an abundance of opportunity for growth and a platform to truly excel.
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