Senior AI Backend Engineer

Senior AI Backend Engineer
Build production AI for the future of financial markets.
Join Version 1 to build intelligent products for a leading US-based financial technology and digital assets business operating across institutional trading, investment management and AI infrastructure.
You will engineer the secure, scalable services behind production AI that supports high-value decisions across trading, risk and portfolio management. Collaborating with an experienced US-based engineering team, you will build Python APIs, real-time data integrations and AI-enabled backend services that solve genuine problems across financial markets. This is AI engineered for measurable business impact.
Working with Financial Services, product and engineering stakeholders in the United States, you will translate business requirements into reliable software and connect applications to an established AI platform powered by Amazon Bedrock, AgentCore and SageMaker.
Why this opportunity stands out
Build production AI services rather than isolated proofs of concept.
Solve genuine challenges across trading, risk and portfolio management.
Own backend services from design and data modelling through to production operation.
Work with Python, AWS, LLMs, RAG, embeddings and real-time data.
Influence architecture, reliability and measurable business outcomes.
Collaborate directly with senior US-based product and engineering teams.
What you will do
Design, build and maintain Python backend services and REST APIs that power Financial Services products.
Design data models and work with relational and other data stores based on the needs of each product.
Integrate backend services with an established AI platform using Amazon Bedrock, AgentCore and SageMaker-based APIs.
Build application features using LLMs, RAG, embeddings, prompt engineering and function calling.
Apply distributed systems and software engineering principles to build secure, scalable and maintainable services.
Write automated tests and maintain CI/CD pipelines for reliable, repeatable deployments.
Diagnose and resolve production issues involving latency, reliability, performance and data quality.
Implement observability across logs, metrics, tracing and alerts, with a focus on operational health and cost efficiency.
Contribute across data pipelines, infrastructure and frontend touchpoints where needed to deliver features end to end.
Partner with product, business, full-stack, data and AI platform teams, and share backend and AI-integration best practices.
We are looking for an experienced backend engineer who combines strong Python and cloud engineering capability with commercial awareness and a genuine interest in Financial Services AI.
Essential experience
7+ years of professional software engineering experience, including ownership of production-grade backend services.
Strong hands-on Python experience and a solid understanding of software engineering and distributed systems fundamentals.
Experience designing and developing secure, scalable REST APIs.
Experience with relational database design, ideally PostgreSQL.
Hands-on experience with AWS or a comparable cloud platform, including building and operating production applications.
Experience with Docker, Kubernetes, Terraform, CI/CD pipelines and automated testing.
Experience with observability, monitoring, reliability engineering, performance tuning and cost optimisation.
Working knowledge of generative AI and LLM concepts, including RAG, embeddings, prompt engineering and function calling.
Experience integrating applications with LLM or AI platform APIs such as Amazon Bedrock or an equivalent service.
Experience implementing authentication, authorisation, SSO, RBAC and secrets management in production applications.
Experience within Financial Services, fintech, capital markets, banking or another regulated environment.
A genuine interest in trading, investment management, digital assets and cryptocurrency.
Confidence communicating technical recommendations and trade-offs to engineering and business stakeholders.
Technology environment
Python | REST APIs | AWS | Amazon Bedrock | SageMaker | AgentCore | RAG | Embeddings | Kubernetes | Terraform | Docker | PostgreSQL | Databricks | CI/CD | Automated testing | Observability | OAuth2/OIDC | SSO | RBAC
You do not need deep model-training or agentic-platform architecture experience. We value strong backend engineering fundamentals, practical AI integration experience and the judgement to build reliable products around an established AI platform.
Valuable additional experience
Capital markets, trading, risk or portfolio management platforms.
Digital assets, cryptocurrency, blockchain or tokenisation.
Amazon Bedrock, SageMaker, AgentCore or comparable AI services.
Vector databases such as pgvector and AI-powered search or retrieval.
Databricks, Jenkins or real-time data pipelines.
Evaluating AI features for accuracy, latency, hallucination handling and graceful degradation.
Awareness of regulatory and compliance considerations in Financial Services technology.
Technical leadership or mentoring across engineering teams.

Employment type
Full-time

Work arrangement
Hybrid