AI Engineer

frameworks such as LangChain, LangGraph, CrewAI, or similar Develop and optimise RAG pipelines, including document ingestion, chunking strategies, embedding generation, retrieval logic, and vector search Implement and manage vector databases such as pgvector on Aurora, OpenSearch, Pinecone, or similar Build and maintain data and ETL pipelines using Apache Airflow, Prefect, or similar tools Develop backend services and APIs in Python / FastAPI to serve AI models, RAG systems, and agent workflows Deploy and manage AI workloads on AWS services such as Bedrock, SageMaker, Lambda, S3, Aurora/RDS, EC2 Work with Docker and Kubernetes to containerise and orchestrate AI workloads Design and execute evaluation frameworks for LLM outputs, including automated testing, LLM-as-judge approaches, and human-in-the-loop review Work with LLM APIs and orchestration tools such as AWS Bedrock, OpenAI API, Anthropic API, or similar Apply prompt engineering, fine-tuning techniques, and

LLM evaluation methodologies Collaborate with domain experts, Data Engineers, Product Managers, and the Tech Lead to turn business requirements into AI solutions Participate in Scrum ceremonies and contribute to a collaborative Agile engineering culture Stay up to date with the rapidly evolving AI/ML ecosystem and proactively propose new tools, improvements, and approaches Mentor junior team members and share AI engineering best practices Must Have 3-5 years of experience in Software Engineering, with at least 1-2 years focused on AI / ML Engineering Strong proficiency in Python Experience with AI/ML and LLM frameworks such as LangChain, LangGraph, Hugging Face, PyTorch, or similar Hands-on experience building RAG systems, including embeddings, vector stores, semantic search, and hybrid search strategies Experience working with LLM APIs such as AWS Bedrock, OpenAI API, Anthropic API, or similar Solid understanding of prompt engineering, fine-tuning techniques, and LLM evaluation methodologies Hands-on experience with AWS services such as EC2, S3, Lambda, Aurora/RDS, Bedrock, SageMaker Experience with Docker and Kubernetes Familiarity with data pipeline tools such as Apache Airflow, Prefect, or similar Experience developing backend services or APIs, ideally with FastAPI Proficiency with Git and software engineering best practices Experience working in a Scrum Agile environment Strong problem-solving, analytical thinking, communication, and teamwork skills Fluent English Nice to Have Experience with multi-agent architectures and protocols such as A2A or MCP Familiarity with MLOps practices: model versioning, experiment tracking, MLflow, Weights & Biases, and CI/CD for ML Experience with observability and evaluation platforms for LLMs such as Langfuse, Datadog LLM Observability, LangSmith Knowledge of graph databases or knowledge graphs for enhanced retrieval Experience with CI/CD pipelines using tools such as GitHub Actions Familiarity with Infrastructure as Code, especially Terraform Experience with code quality and security tools such as SonarCloud, Snyk Experience in aviation, travel, or large-scale digital environments Spanish language skills are a plus Hybrid model - 2 days onsite per week Why join this project? People first - diverse and inclusive culture in an international environment. Build production-ready LLM applications, RAG systems, and agentic AI solutions Work with cutting-edge AI technologies across the LLM, agents, vector search, and AWS ecosystem ️ Contribute to scalable engineering practices around AI applications, data pipelines, evaluation, and deployment ️ Gain hands-on exposure to AWS-native AI services such as Bedrock, SageMaker, Lambda, S3, and Aurora Be part of a fast-moving AI environment where experimentation, ownership, and impact are highly valued High team stability and collaborative culture. €1200 per year training budget and continuous learning opportuni