Ai Engineer

We are looking for an AI Engineer to join a dynamic AI team within an international technology environment.In this role, you will design, build, and deploy LLM-powered applications, RAG pipelines, multi-agent systems, and scalable AI solutions on AWS.You will work at the intersection of software engineering, AI engineering, data pipelines, and cloud deployment, contributing to real use cases across the group. This role is highly hands-on and focused on building intelligent systems that move from experimentation to production.You will work closely with Data Scientists, Platform and DevOps Engineers, Data Engineers, domain experts, Product Managers, and the Tech Lead to move AI solutions from experimentation into production.If you enjoy working with LLMs, agents, RAG, vector databases, Python APIs, and AWS-native AI services, this could be a great fit.What you’ll doDesign, build, and maintain LLM-powered applications and multi-agent systems primarily using LangGraph and LangChain, alongside tools such as CrewAI or similar frameworksDevelop and optimise RAG pipelines, including document ingestion, chunking strategies, embedding generation, retrieval logic, and vector searchImplement and manage vector databases such as pgvector on Aurora, OpenSearch, Pinecone, or similarBuild and maintain data and ETL pipelines using Apache Airflow, Prefect, or similar toolsDevelop backend services and APIs in Python / FastAPI to serve AI models, RAG systems, and agent workflowsDeploy and manage AI workloads on AWS services such as Bedrock, SageMaker, Lambda, S3, Aurora/RDS, EC2Work with Docker and Kubernetes to containerise and orchestrate AI workloadsDesign and execute evaluation frameworks for LLM outputs, including automated testing, LLM-as-judge approaches, and human-in-the-loop reviewWork with LLM APIs and orchestration tools such as AWS Bedrock, OpenAI API, Anthropic API, or similarApply prompt engineering and LLM evaluation methodologies, and assess when fine-tuning or other adaptation techniques are appropriate.Collaborate with domain experts, Data Engineers, Product Managers, and the Tech Lead to turn business requirements into AI solutionsParticipate in Scrum ceremonies and contribute to a collaborative Agile engineering cultureStay up to date with the rapidly evolving AI/ML ecosystem and proactively propose new tools, improvements, and approachesSupport less experienced team members and share AI engineering best practices across the team.Must Have3-5 years of experience in Software Engineering, with at least 1-2 years focused on AI / ML EngineeringStrong proficiency in PythonExperience with AI/ML and LLM frameworks such as LangChain, LangGraph, Hugging Face, PyTorch, or similarHands-on experience building RAG systems, including embeddings, vector stores, semantic search, and hybrid search strategiesExperience working with LLM APIs such as AWS Bedrock, OpenAI API, Anthropic API, or similarSolid understanding of prompt engineering, fine-tuning techniques, and LLM evaluation methodologiesHands-on experience with AWS services such as EC2, S3, Lambda, Aurora/RDS, Bedrock, SageMakerExperience with observability and evaluation platforms for LLMs such as Langfuse, Datadog LLM Observability, LangSmithExperience with Docker and KubernetesFamiliarity with data pipeline tools such as Apache Airflow, Prefect, or similarExperience developing backend services or APIs, ideally with FastAPIProficiency with Git and software engineering best practicesExperience working in a Scrum Agile environmentStrong problem-solving, analytical thinking, communication, and teamwork skillsNice to HaveExperience with multi-agent architectures and protocols such as A2A or MCPFamiliarity with MLOps practices: model versioning, experiment tracking, MLflow, Weights & Biases, and CI/CD for MLKnowledge of graph databases or knowledge graphs for enhanced retrievalExperience with CI/CD pipelines using tools such as GitHub ActionsFamiliarity with Infrastructure as Code, especially TerraformExperience with code quality and security tools such as SonarCloud, SnykExperience in aviation, travel, or large-scale digital environmentsSpanish language skills are a plusHybrid model - 2 days onsite per weekWhy join this project?People first - diverse and inclusive culture in an international environment.Build production-ready LLM applications, RAG systems, and agentic AI solutionsWork with cutting-edge AI technologies across the LLM, agents, vector search, and AWS ecosystemContribute to scalable engineering practices around AI applications, data pipelines, evaluation, and deploymentGain hands-on exposure to AWS-native AI services such as Bedrock, SageMaker, Lambda, S3, and AuroraBe part of a fast-moving AI environment where experimentation, ownership, and impact are highly valuedHigh team stability and collaborative culture.€** per year training budget and continuous learning opportunities.Flexible compensation model.Private health insurance and benefits package.Flexible working hours and hybrid model.Wellhub: fitness, wellness, and mental health support.Football and paddle tennis teams sponsored by Capitole.Team buildings, global events, and strong tech communities.Information Security NoticeThe employee will have access to confidential information related to Capitole and the assigned project.Compliance with internal security and information protection policies is mandatory.

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 observability and evaluation platforms for LLMs such as Langfuse, Datadog LLM Observability, LangSmith 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 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 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