Ai Engineer

We are looking for anAI Engineerto join a dynamic AI team within an international technology environment.In this role, you will design, build, and deployLLM-powered applications, RAG pipelines, multi-agent systems, and scalable AI solutionson AWS.You will work at the intersection ofsoftware 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 withLLMs, agents, RAG, vector databases, Python APIs, and AWS-native AI services , this could be a great fit.What you’ll doDesign, build, and maintainLLM-powered applicationsand multi-agent systems primarily usingLangGraphandLangChain , alongside tools such as CrewAI or similar frameworksDevelop and optimiseRAG pipelines , including document ingestion, chunking strategies, embedding generation, retrieval logic, and vector searchImplement and manage vector databases such aspgvector on Aurora, OpenSearch, Pinecone , or similarBuild and maintain data and ETL pipelines usingApache Airflow, Prefect , or similar toolsDevelop backend services and APIs inPython / FastAPIto serve AI models, RAG systems, and agent workflowsDeploy and manage AI workloads onAWSservices such asBedrock, SageMaker, Lambda, S3, Aurora/RDS, EC2Work withDocker and Kubernetesto 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 asAWS 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.3-5 years of experience in Software Engineering , with at least1-2 years focused on AI / ML EngineeringStrong proficiency inPythonExperience with AI/ML and LLM frameworks such asLangChain, LangGraph, Hugging Face, PyTorch , or similarHands-on experience buildingRAG systems , including embeddings, vector stores, semantic search, and hybrid search strategiesExperience working withLLM APIssuch asAWS Bedrock, OpenAI API, Anthropic API , or similarSolid understanding ofprompt engineering, fine-tuning techniques, and LLM evaluation methodologiesHands-on experience withAWSservices such asEC2, S3, Lambda, Aurora/RDS, Bedrock, SageMakerExperience with observability and evaluation platforms for LLMs such asLangfuse, Datadog LLM Observability, LangSmithExperience withDocker and KubernetesFamiliarity with data pipeline tools such asApache Airflow, Prefect , or similarExperience developing backend services or APIs, ideally withFastAPIProficiency withGitand software engineering best practicesExperience working in aScrum AgileenvironmentStrong problem-solving, analytical thinking, communication, and teamwork skillsExperience withmulti-agent architecturesand protocols such asA2A 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 asGitHub ActionsFamiliarity with Infrastructure as Code, especiallyTerraformExperience with code quality and security tools such asSonarCloud, 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.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.#J-***-Ljbffr

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 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