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Vacantes / Aveni

Senior AI Software Engineer

Aveni · United Kingdomsenior

En corto

  • ▸Ingeniero de software senior especializado en sistemas de IA productivos para servicios financieros.
  • ▸Construyes y despliegas productos con LLMs, RAG y flujos agente en React, Node.js y AWS, con enfoque en operaciones y observabilidad.
  • ▸Trabajas con FinLLM (primer LLM del Reino Unido para servicios financieros) sin entrenar modelos.

Proficiency in English is required.

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¿Qué piden?

  • ✓Experiencia sólida en software de producción con aplicaciones de IA/LLM.
  • ✓Dominio de Node.js, TypeScript y React para frontend y backend.
  • ✓Experiencia directa con RAG, flujos agente, ingeniería de prompts y orquestación de LLMs.
  • ✓Conocimiento profundo de arquitecturas de microservicios, eventos y AWS.
  • ✓Compromiso con pruebas automatizadas, CI/CD y calidad del código.
  • ✓Habilidades de comunicación y mentoría técnica.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

Node.jsTypeScriptReactAWSLambdaECS/FargateAPI GatewaySQS/SNSDynamoDBRDS

¿A quién escribirle en Aveni?

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Aveni is redefining how financial institutions use AI. We build real, production-ready agentic systems that reshape how banks, advisers, and financial services firms operate, replacing fragmented processes with intelligent, end-to-end workflows. Our achievements include being recognised as Fintech of the Year for two consecutive years and winning Best Specialist Technology Service at the Professional Adviser Awards 2025. Backed by leading investors, including Puma Private Equity, and commercially supported by Lloyds Banking Group and Nationwide Building Society, we previously completed one of Scotland’s largest Series A rounds. As the adoption of AI accelerates in financial services, we’re scaling our team to meet growing demand. The Role We're seeking a hands-on Senior AI Software Engineer with strong full-stack experience to build and ship AI-powered products into production, leveraging FinLLM (the UK's first LLM for Financial Services) and other LLMs. You won’t build or train models—instead, you'll engineer the systems, services, interfaces, and integrations that bring those models to life for advisers, analysts, and banking teams. You'll own features end-to-end, covering selection, implementation, deployment, observability, and iteration across backend, frontend, and cloud infrastructure. Responsibilities Build production-ready, AI-powered products integrating LLM capabilities, RAG, and agentic workflows into real-world processes. , LangGraph, Langfuse). Design evaluation, monitoring, and observability to ensure reliability and readiness for production. js and TypeScript. Build modern, responsive React frontends to make AI useful in customer workflows. Architect cloud-native systems on AWS (Lambda, ECS/Fargate, API Gateway, SQS/SNS, DynamoDB, RDS). Implement microservices, distributed-system principles, and strong operational controls for integration into financial products. Uphold engineering best practices: clean code, automated testing, CI/CD, and operational excellence. Diagnose and improve production behaviours relating to reliability, latency, cost, and AI quality. Mentor engineers, contribute to team growth, and collaborate closely with product and engineering to deliver customer-ready features. What We’re Looking For Essential Experience Extensive software engineering background, building, deploying, and running production LLM/AI applications. js, TypeScript, and production React. Experience with prompt engineering, agentic workflows, RAG, and LLM integration. Knowledge of AI evaluation frameworks, monitoring, and observability. Deep understanding of AWS, cloud-native engineering, APIs, and integration patterns. Experience with microservices, messaging patterns, event-driven, and distributed architectures. Commitment to quality, clean code, testing, and operational excellence. Nice to Have Experience with NestJS or similar backend frameworks. Familiarity with LangGraph, Langfuse, or other AI orchestration and observability tools. Knowledge of AI implementation in regulated/high-trust environments, ideally in financial services. Systematic evaluation design for LLM outputs and agentic workflows. Experience delivering agentic systems in production. Skills & Mindset Effective working in ambiguity and complex problem-solving. Designing robust solutions and making pragmatic technical choices. Clear communication with technical and non-technical stakeholders. Awareness of production AI challenges: reliability, latency, and cost. Technical mentorship, influencing decisions, and raising team standards. Qualifications Relevant degree or equivalent practical experience (production engineering capability is more important than a specific academic background). Benefits Remote-first working across the UK Work abroad policy Co-working spaces av

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