Senior Ai Backend Engineer - Bme | Bolsas Y Mercados Españoles

Bme | Bolsas Y Mercados Españoles
Madrid, Spain
3 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience required
5 years minimum
Working hours
Regular working hours
Languages
English

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Microsoft Azure Continuous Integration Data Mining Software Design Patterns DevOps Python (Programming Language) PostgreSQL Parsing Query Optimization Azure DevOps Pipelines
+20 more
Software Engineering SQLAlchemy Systems Architecture Systems Integration Google Cloud Large Language Models Multi-Agent Systems Indexer Backend Gitlab Fastapi Build Management Pytest Git Flow Information Technology Graphql Machine Learning Operations Front End Software Development Terraform Docker

Job description

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 DoProductize 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 BringBackend 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.#J-*****-Ljbffr

Requirements

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. #J-*****-Ljbffr

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