Senior AI Backend Engineer

BME | Bolsas y Mercados Españoles; Madrid; Presencial; EUR 55.000 - 75.000; Jornada completa

Descripción de la vacante

BME | Bolsas y Mercados Españoles is seeking a Senior AI Backend Engineer to join the AI Engineering team. This role focuses on backend ownership, APIs, and integrations for AI productization in GenAI and Data Mining areas.

The ideal candidate will have deep proficiency in Python, with experience in developing modern APIs and integrating LLMs into production systems. Candidates should have 5+ years in backend development and a relevant degree, with fluency in English.

Formación

• Deep proficiency in Python and hands-on experience designing and scaling modern APIs like FastAPI.
• Production experience with GraphQL and AI APIs (OpenAI, Gemini, Anthropic).
• Strong production experience with PostgreSQL, including schema design and efficient queries.
• Solid working experience with Google Cloud Platform and/or Azure and CI/CD pipelines.
• Strong testing discipline with pytest and habit of writing maintainable, well-typed code.
• 5+ years in Backend Development or Systems Architecture and relevant degree required.

Responsabilidades

• Build the backends and infrastructure that turn AI prototypes into reliable production products.
• Architect and maintain endpoints and GraphQL APIs for multiple services.
• Develop ingestion and vendor-LLM integrations with high concurrency and state-machine workflows.
• Partner with MLOps on CI/CD to establish performance baselines for APIs and AI workloads.
• Apply clean architecture and design patterns, enforce standards through tests and reviews.

Conocimientos

Backend Expertise in Python

AI Productization Experience

Data & Persistence

Cloud & DevOps

Quality & Collaboration

Educación

Relevant degree in Computer Science or related field

Herramientas

PostgreSQL

Terraform

Docker

Git

GraphQL

Descripción del empleo

Join SIX Financial Information's AI Engineering team as a Senior AI Backend Engineer of our Productization squad. You will own the backend, APIs, and integrations that turn AI prototypes into production-grade products in our GenAI and Data Mining lines.

This is a high-leverage, player-coach role at the heart of how we scale AI delivery. You will set the technical direction for production-grade engineering, partner with data scientists and AI engineers on serving and integration patterns, and free the AI Solutions team to focus on models and evals while your squad owns reliable delivery to end users.

What You Will Do

• Productize AI & GenAI Solutions: Build the backends, agent/RAG infrastructure, MCP servers, and LLM-serving layers that turn models, prompts, and pipelines into reliable, observable production products.
• Design & Build Production APIs: Architect and maintain endpoints and GraphQL APIs, defining schema, resolver, and authentication patterns that serve frontend applications, internal services, and AI features.
• Integrate Data & AI Pipelines: Develop ingestion, parsing, OCR, and vendor-LLM integrations (OpenAI, Gemini, Anthropic) with strong attention to concurrency, async processing, retries, idempotency, and state-machine workflows.
• Operate in Production: Partner with MLOps on Docker, Terraform, and Azure DevOps CI/CD; establish performance baselines and observability for both APIs and AI workloads; optimize for latency, throughput, and cost.
• Champion Quality & Coach Peers: Apply clean architecture and proven design patterns, enforce standards through tests and reviews, and coach junior engineers and rotating contributors.

What You Bring

• Backend Expertise: Deep proficiency in Python and hands-on experience designing and scaling modern APIs (e.g. FastAPI) in production, including async/await for I/O-bound workloads. Production experience with GraphQL (Strawberry or comparable) is a strong plus.
• AI Productization Experience: Hands-on experience integrating LLMs and AI APIs (OpenAI, Gemini, Anthropic) into production systems, with familiarity in RAG, agent frameworks, vector databases, MCP, or ML serving patterns.
• Data & Persistence: Strong production experience with PostgreSQL, SQLModel/SQLAlchemy, and Alembic, including schema design, indexing, and efficient queries.
• Cloud & DevOps: Solid working experience with Google Cloud Platform and/or Azure, Terraform, Docker, Git workflows, and CI/CD pipelines (Gitlab or equivalent).
• Quality & Collaboration: Strong testing discipline with pytest, a habit of writing maintainable, well-typed code, and the communication skills to mentor peers and partner closely with data scientists and AI engineers.
• Experience & Education: 5+ years in Backend Development, Software Engineering, or Systems Architecture, including significant experience designing and operating production systems. A relevant degree in Computer Science or related field is required. English fluency is required.