GenAI Engineer

An opportunity for an experienced Full-Stack GenAI Engineer to join a leading financial institution in the Netherlands. In this role, you will help design, build and deploy scalable generative AI solutions that support both internal users and customer-facing services. You will work across the full development lifecycle, from data ingestion and backend engineering to integration, deployment, monitoring and continuous improvement.This position is suitable for a strong software engineer with hands-on experience building production-grade AI or cloud-native applications. You should be comfortable working in an innovative, mostly greenfield environment where priorities can change quickly. The ideal candidate combines technical depth in Python, Azure and GenAI technologies with ownership, resilience and the ability to collaborate effectively with engineers, data scientists, product owners and business stakeholders.Responsibilities:Design, develop and deploy scalable and cost-effective GenAI solutions using Azure services such as Azure OpenAI, Azure Machine Learning, Azure Functions and Azure Kubernetes Service.Build full-stack applications and reusable components that can be adopted by multiple engineering teams.Develop Python-based microservices, APIs and automation tools to support GenAI workflows and infrastructure provisioning.Implement AI agents and intelligent workflows using frameworks such as LangChain or Semantic Kernel.Develop solutions involving prompt chaining, agent orchestration, memory management and Retrieval-Augmented Generation.Integrate GenAI capabilities into existing platforms, services and customer-facing applications.Build and maintain CI/CD pipelines using GitHub Actions, Azure DevOps, Terraform and Bicep.Work with containerised applications and orchestration technologies such as Docker and Kubernetes.Implement monitoring, evaluation frameworks, bias detection and drift monitoring to ensure reliable AI performance.Ensure that GenAI workloads meet security, compliance, scalability and performance requirements.Collaborate with data scientists, engineers, product owners and business stakeholders to translate requirements into technical solutions.Participate in architecture discussions, code reviews, pair programming, sprint planning and technical knowledge-sharing sessions.Convert use-case-specific solutions into reusable building blocks that can be used across the wider organisation.Support and mentor junior and medior engineers while contributing to strong engineering standards and development practices.Requirements:4–6 years of experience in software engineering, cloud engineering, DevOps or infrastructure automation.Strong hands-on experience with Python for backend development, API development, automation and AI integration.Experience building and deploying GenAI models, applications or services in a production environment.Practical knowledge of Azure services, preferably including Azure OpenAI, Azure Machine Learning, Azure Functions and AKS.Experience with Infrastructure as Code using Terraform and/or Bicep.Experience building and maintaining CI/CD pipelines with GitHub Actions or Azure DevOps.Knowledge of containerisation and orchestration using Docker and Kubernetes.Experience with GenAI frameworks such as LangChain or Semantic Kernel.Understanding of AI agents, workflow orchestration, prompt chaining and memory management.Familiarity with vector databases and Retrieval-Augmented Generation patterns.Exposure to MLOps practices, model evaluation and AI lifecycle management.Knowledge of API gateways such as Kong is beneficial.Understanding of cloud security, compliance and secure software development practices.Strong software engineering fundamentals, including clean code, testing, design patterns and maintainable architecture.Ability to take ownership, solve complex problems and deliver results in an environment with changing priorities.Strong communication and stakeholder management skills.An entrepreneurial mindset and the confidence to propose, defend and improve technical ideas.A collaborative approach and willingness to support the development of less experienced colleagues.