Top Artificial Intelligence Jobs in Amsterdam

Lead the evolution of Mollie’s operating model and ways of working as the company scales. Own the Mollie Playbook rollout, organizational information architecture, post-merger integration with GoCardless, leadership workshops, change management, and employee feedback loops. Build and manage a small team responsible for rollout, content, training, and adoption. Partner with People, product, engineering, data, and executive leadership to establish effective structures, communication standards, routines, and information access across the organization.

Leads end-to-end photo studio operations, including capacity planning, scheduling, resource allocation, workflow optimization, KPI reporting, technology and AI implementation, vendor management, budgeting, and cross-functional collaboration. Oversees photographers, stylists, retouchers, and producers while improving production efficiency, quality, scalability, and cost-effectiveness.

Own the technical direction of AI-assisted educational content products from discovery through production. Build full-stack Python services, APIs, data flows, interfaces, infrastructure, and robust LLM or agent workflows. Translate editorial research into experiments and durable architectures, establish reliability and security standards, operate production systems, and mentor engineers. Collaborate with technical, editorial, and publishing stakeholders while delivering dependable software that improves content quality, speed, cost, or editorial effort.

Build and own AI-powered educational content tools from prototype to production. Develop Python services, APIs, data flows, interfaces, infrastructure, LLM and agent workflows, evaluations, safeguards, and reusable platform components. Collaborate with engineers, editors, and publishers to improve content workflows, conduct experiments, integrate systems, deploy and monitor software, and make architecture and technical decisions. The role requires pragmatic product thinking, independent ownership, and full-stack engineering across backend, frontend, and infrastructure.

Owns sales of the Salesforce platform to named mid-market accounts in the Netherlands. Develops account strategies, identifies and qualifies opportunities, researches customer challenges, presents technology solutions, and builds value propositions. Partners with sales engineers, solution architects, product specialists, and extended account teams to drive quota attainment and customer success. Requires Dutch and English fluency, Salesforce expertise, technology solution-selling experience, strong communication, and strategic account-planning skills.

Lead product and operations for Fin.ai to drive autonomous, auditable resolution of merchant support queries. Own roadmap, quality metrics (resolution rate, CSAT), team of AI support specialists, cross-functional integration with Engineering/Data, and compliance controls for payments regulations.

Leads customer workshops and AI transformation engagements for manufacturing organizations. Advises executive stakeholders on AI, data, cloud, and digital strategy; designs secure enterprise architectures; develops prototypes and demonstrations; and partners with sales and engineering teams to connect innovation opportunities with measurable business outcomes.

Design, build, and operate agentic AI solutions for autonomous network optimization. Responsibilities include developing reliable AI/ML pipelines, integrating models into real-time network environments, implementing monitoring and governance, validating software quality, and maintaining production systems. The role collaborates with network experts, data engineers, and software teams while exploring reinforcement learning and emerging AI technologies.

Designs and operationalizes AI platforms on Microsoft Azure, including serverless compute, Azure Databricks, CI/CD pipelines, observability, monitoring, and cost optimization. Develops scalable AI and data engineering solutions using Python, PySpark, and SQL, while applying MLOps, Terraform, enterprise security, data protection, and compliance practices. Collaborates with data science, cloud engineering, and DevOps teams to integrate AI models and services, including Mistral AI LLMs, into corporate environments.

Lead the EMEA AI Forward Deployed Engineering team, focusing on AI transformation and building customer-facing AI applications while managing key accounts and developing team structure.

Lead engineering execution and technical direction while remaining approximately 75% hands-on. Responsibilities include developing LLM- and AI-powered products, coding, code review, debugging, platform performance, sprint planning, cross-functional coordination, technical mentorship, recruiting, and supporting engineers’ growth. The role partners closely with founders and product teams and offers a path toward Engineering Manager responsibilities.

Lead the initiation, planning, and implementation of complex programs. Build roadmaps, manage project managers, track objectives, benefits, costs, risks, and dependencies, and report progress to business leadership. The role also oversees core team effectiveness and supports clients with ERP, data, and AI-related transformation initiatives.

Design and improve mobile health coaching experiences focused on behavior change, AI-generated coaching, gamification, and user engagement. Collaborate cross-functionally to develop innovative UI/UX solutions, using user psychology, UX research, data analytics, and rapid experimentation to inform decisions. Generate and present original design hypotheses, validate solutions, and improve customer journeys, motivation, habits, and long-term loyalty.

Own product areas for AI Stockbroking and other financial services. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, compliance, operations, and business teams; simplify complex financial products; launch features; monitor performance; and continuously improve products using data, research, feedback, and production insights.

Grow Qure’s user base from scratch by testing acquisition channels, managing budgets, creating marketing assets, building measurable funnels, and partnering with product on activation-focused improvements. The role requires rapid experimentation across paid, organic, community, video, and content channels, using data to optimize acquisition economics and eliminate underperforming efforts.

Leads global commercial customer data governance, Customer 360 strategy, segmentation, data quality, reporting, and insight initiatives. Owns customer definitions and hierarchies across SAP and Salesforce, establishes governance and KPI standards, and aligns reporting and analytics investments with business value. Partners cross-functionally to improve data quality, reporting adoption, forecast accuracy, and AI readiness while driving change management and value realization across the commercial organization.

Lead AI Value Analyst responsible for building and maintaining business cases for AI initiatives, estimating and tracking benefits, and reporting ROI. Partners with stakeholders, Finance, and AI CoE to quantify productivity, cost, revenue, and risk impacts, prioritize opportunities, validate data and assumptions, and produce dashboards and presentations to communicate realized value and governance across the AI portfolio.

Own AI Investing product areas across financial services. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams. Simplify complex financial workflows, launch and monitor features, analyze feedback and performance, and drive iterative improvements while balancing speed, user experience, business impact, compliance, and operational complexity.

Lead workshops, architect and design Workday Extend applications, deliver requirements and design to developers, manage and guide Extend consultants, liaise with clients and Workday, and support pre-sales and AI-enabled solutioning.

Support the development, testing, deployment, and improvement of AI solutions, including LLM applications, RAG pipelines, prompts, AI agents, and orchestration workflows. Prepare data and knowledge foundations, research emerging models and frameworks, translate business needs into AI use cases, monitor solution performance, and document methodologies. Collaborate with senior technical professionals, data teams, and business stakeholders in an international environment.

Design and support scalable AI infrastructure, applications, data pipelines, and cloud-based solutions. Collaborate with technical and business stakeholders, contribute to architecture and documentation, support deployments and testing, monitor performance and costs, troubleshoot issues, and help manage technical risks. The role requires cloud, DevOps, data engineering, infrastructure-as-code, containerization, CI/CD, monitoring, and AI/ML platform experience.

Supports the development, testing, deployment, and monitoring of AI solutions. Responsibilities include preparing data and knowledge foundations, building RAG pipelines, testing prompts and context strategies, researching AI models and tools, contributing to AI agents and orchestration workflows, translating business needs into use cases, and documenting solutions. The role collaborates with data, AI, technical, and business teams while continuously improving AI application performance and reliability.

Lead hands-on development of AI-powered workflows, coding-agent integrations, and C++ development capabilities for CLion. Design agent architectures, tool integrations, benchmarks, and evaluation frameworks while prototyping debugging, code understanding, refactoring, and navigation workflows. Collaborate with JetBrains AI, ML, product, QA, and C++ teams to shape technical direction, roadmap priorities, and AI strategy for developer tools.

Build and deploy production-grade AI agents and tool-calling workflows on GCP using governed business data. Identify high-value automation opportunities, develop reusable skills, connectors, evaluations, and templates, and establish standards for accuracy, cost, guardrails, and sensitive-data handling. Partner directly with business teams, create runbooks and ownership models, mentor team champions, and demonstrate measurable operational impact through production AI systems.

Design, build, and refine AI solutions from prototype through production. Develop context layers using knowledge graphs, ontologies, governance, data lineage, and RAG architectures. Apply prompt, context, memory, and harness engineering; translate business challenges into AI workflows; contribute to agentic systems and orchestration architectures; evaluate emerging AI tools; and document reusable methodologies and standards.