Senior AI Platform Backend Engineer (LLM)

Hybrid in Spain

Machine Learning Engineering

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We are seeking a talented and proactive Senior AI Platform Backend Engineer (LLM) to join our team in Spain in a remote working mode and to lead the design, optimization and deployment of machine learning pipelines using MLOps in cloud environments. In this role, you will implement LLM-based solutions for chatbots and Retrieval-Augmented Generation (RAG) systems and develop robust DevOps/MLOps pipelines for production.

This position offers a flexible work setup, allowing for remote work or a hybrid arrangement, with occasional office visits.

Responsibilities

Maintain and enhance CI/CD pipelines using tools such as GitHub Actions, AWS CodePipeline, Jenkins or ArgoCD
Design and develop backend architecture for AI Verification and ChatGPT services utilizing Python and FastAPI
Build, optimize and scale classifiers and tools leveraging machine learning, encoders and rule-based models
Architect and implement solutions following Domain-Driven Design (DDD) and Test-Driven Development (TDD) best practices
Design, develop, and maintain a production-grade LLM-as-a-judge service for verifying AI-generated content from source documents, leveraging frameworks such as HuggingFace Transformers, SpaCy, NLTK, and BM25
Build and maintain high-throughput Retrieval-Augmented Generation (RAG) services, including ingestion pipelines and message brokers
Perform prompt engineering, including techniques such as Chain-of-Thought and Few-Shot Learning, across various LLMs (OpenAI, Anthropic, Google, etc.)
Provide hands-on expertise and support for one or more leading AI frameworks (TensorFlow, Keras, PyTorch, BERT, etc.)
Demonstrate technical leadership in at least one AI specialization, such as graph recommendation systems, deep learning or natural language processing

Requirements

Strong proficiency in Python and experience developing backend services with FastAPI
Experience building and maintaining CI/CD pipelines using tools such as GitHub Actions, AWS CodePipeline, Jenkins or ArgoCD
Hands-on experience designing and optimizing scalable machine learning models, including classifiers, encoders and rule-based systems
Experience working with large language models (LLMs) and frameworks such as HuggingFace Transformers, Spacy, NLTK and BM25
Proven ability to design, develop and maintain high-load Retrieval-Augmented Generation (RAG) services, including ingestion pipelines and message brokers