Top Artificial Intelligence Jobs in the Netherlands

Leads research and advisory work for CIOs on data, analytics, and AI strategy, governance, operating models, architecture, value realization, and emerging technologies. Develops thought leadership, market analysis, presentations, and actionable client guidance; evaluates agentic and composite AI, modern data platforms, AI-ready data, organizational maturity, and capability-building needs. Represents Gartner at client meetings and conferences, supports sales, mentors colleagues, and shapes research agendas.

Develop AI models, algorithms, and research software for biomolecular modeling and simulation. Responsibilities include integrating heterogeneous molecular and laboratory data, developing deep learning approaches, orchestrating large-scale data generation and training runs, writing research code, and communicating findings through technical papers and presentations. The role requires collaboration with an interdisciplinary team focused on molecular sciences, computational chemistry, biology, and machine learning.

Build and scale low-latency AI serving infrastructure for SLMs, prompt-injection engines, and classification services. Architect GPU-accelerated inference pipelines, backend microservices, Kubernetes clusters, rate limiting, tenant guardrails, monitoring, and autoscaling systems. Develop continuous evaluation, retraining, model CI/CD, regression gates, and MLOps/LLMOps pipelines while improving service reliability, latency, cost efficiency, and detection quality.

Build and operate production LLM agents for hospitality software, including orchestration, tool calling, memory, retrieval, data pipelines, evaluation, observability, and security controls. Develop reliable data foundations over financial and operational information, integrate backend services, monitor agent quality and performance, and collaborate with product teams to deliver useful customer and internal workflows.

Six-month internship investigating how AI can improve a global HR service desk. Responsibilities include analyzing ticket and process data, mapping workflows, interviewing stakeholders, prioritizing AI use cases, exploring automation and knowledge retrieval solutions, testing a proof of concept, and presenting implementation recommendations.

Own the business definitions, territory and quota processes, GTM reporting, forecasting, and revenue operations infrastructure. Lead embedded RevOps Specialists and partner with Sales, Customer Success, Marketing, Finance, and GTM Engineering. Administer HubSpot as the GTM source of truth, establish forecasting methodologies, run business reviews, translate business needs into technical briefs, and drive AI-assisted automation and reporting.

Supports EMEA Quality Assurance digital communication initiatives by optimizing SharePoint user experience, conducting usability assessments, creating visual content, integrating Power BI dashboards, improving digital QMS solutions, and exploring AI-enabled workplace tools. Collaborates with Quality Assurance, IT, Communications, and Operational Excellence stakeholders to improve employee engagement, communication, and visibility of quality initiatives.

Supports the Performance Marketing team in identifying, testing, and implementing AI-powered workflows. Responsibilities include automating campaign management and reporting tasks, exploring Databricks and Genie, validating AI-generated insights, documenting tools and prompts, gathering user feedback, and promoting responsible AI adoption.

Build and own SevenLab’s outbound and revenue operations system. Audit and improve Attio, Clay, email sequences, partner motions, enrichment workflows, signal-based lists, and reporting. Define ICPs, launch Dutch-market outbound, test sequences, manage deliverability, measure results, and generate qualified meetings for the Account Executive. The role also involves prospect intro calls, GDPR-compliant data sourcing, documentation, and collaboration with commercial and delivery teams.

Design, train, evaluate, optimize, and deploy machine learning models for quantum error correction. Build data pipelines and training infrastructure, work with QEC researchers and engineering teams, and translate research concepts into scalable production tools. The role includes model experimentation, hardware-constrained optimization, technical communication, and contributions to publications or conference presentations.

Leads a 10+ person AI-native R&D engineering team while remaining hands-on with coding, architecture, prototypes, distributed systems, and production readiness. Owns initiatives from ambiguous requirements through implementation, evaluation, validation, documentation, and handoff. Establishes responsible AI-assisted development practices, mentors engineers, maintains high standards for security, testing, reliability, and code quality, and partners with Product, Architecture, Security, and Engineering stakeholders.

Leads enterprise Celonis implementations using AI-assisted delivery. Conducts discovery workshops, translates business needs into technical specifications, configures data models, process analyses, KPIs, dashboards, automations, and operational solutions, validates AI-generated outputs, quantifies business value, trains customers, and manages project outcomes. Requires deep process or industry expertise, strong consulting and analytical skills, proficiency in PQL, SQL, and Python, and willingness to travel up to 50%.

Own and expand strategic enterprise accounts across Europe, serving as executive relationship owner and driving revenue growth through account expansion plans. Coach account managers and customer success teams on strategy and complex commercial execution, improve key account processes, collaborate with product and customer operations, and report performance and pipeline to leadership. Represent the company with utility customers at conferences and events.

Six-month intern supporting Client and Market Development while exploring practical AI applications, process improvements, and technology-driven workflows. Responsibilities include testing AI-supported solutions, researching markets and competitors, preparing pitches and presentations, supporting client initiatives, sharing AI knowledge, and collaborating with lawyers, partners, and business development colleagues.

Leads client-facing AI consulting engagements by analyzing business needs, designing measurable AI strategies and RAG or agentic solutions, facilitating executive workshops, and advising on adoption, governance, quality, and enterprise architecture. Collaborates with architects and engineering teams, supports proposals and solution design, evaluates AI reliability, and develops reusable offerings, methodologies, and accelerators while building trusted client relationships.

Leads Prosus’s product communications function for AI products, owning positioning, messaging, launches, media and analyst relationships, owned channels, and cross-functional alignment. Builds the communications discipline through playbooks, processes, measurement, and standards. Translates complex technology into compelling narratives for technical and non-technical audiences while connecting communications to adoption, reputation, and business results.

Develop and evolve data, privacy, and AI governance frameworks; translate regulatory and policy requirements into practical guidance; establish stewardship models, governance forums, and supporting tooling; deliver data and AI literacy programs; and advise cross-functional teams on responsible AI, bias, transparency, explainability, data quality, lineage, and regulatory compliance.

Develop R automation and AI solutions supporting pharmaceutical clinical trials. Interpret requirements, identify workflow improvements, build proof-of-concept tools using R, Shiny, and LLM-supported coding, and mature successful prototypes into validated, documented, reusable solutions. Collaborate with statisticians, programmers, data scientists, and automation leads while ensuring compliance with clinical development standards, including testing, traceability, audit trails, version control, and regulated delivery expectations.

Build and scale an AI-first sales enablement and certification function for a multi-product B2B SaaS portfolio. Create field-ready messaging, learning content, assessments, and certifications for account executives and business development representatives. Partner with solution engineers, subject matter experts, and designers; establish win/loss feedback loops; and use performance data to improve adoption, sales execution, and behavior change.

Designs and standardizes reusable AI platform services, SDKs, templates, architectures, and governance patterns. Builds scalable cloud-native AI capabilities, CI/CD pipelines, platform automation, and integrations across Azure, AKS, and supporting platforms. Develops ML, agentic AI, generative AI, RAG, MLOps, and LLMOps solutions while improving developer experience, observability, security, compliance, and reliability across the organization.

Build and operate production AI products, including sales acceleration systems, recommendation engines, and agentic operating systems. Own solutions end to end by gathering user needs, designing, coding, deploying, monitoring, and maintaining them. Evaluate agent decisions, detect performance drift, enable non-engineers to create automations, and contribute to shared AI infrastructure. Work across TypeScript, Rust, Ruby, Google Cloud, Kubernetes, and infrastructure as code while collaborating across advisory, sales, operations, and engineering.

Designs and delivers AI-native solutions across business functions including People, Legal, Finance, and Operations. Responsibilities include identifying transformation opportunities, building and deploying AI agents and automated workflows, integrating enterprise systems, driving adoption, measuring business impact, applying governance and human oversight, and mentoring colleagues. The role partners with functional leaders to redesign operating models and continuously improve production AI solutions.

Build and operate secure, scalable payment services and third-party integrations while developing AI-powered and agentic workflows. Responsibilities include payment processing, reconciliation, settlement, tool calling, retrieval, permissions, guardrails, observability, testing, performance optimization, and technical architecture. The role requires Golang, production software engineering, distributed systems, databases, REST APIs, payment or POS integrations, LLM applications, and secure AI development practices.

Own and operate the AI security runtime control plane across Azure, AWS, and GCP. Build inline AI enforcement, tune prompt-injection, data protection, tool-call, and agent access policies, and integrate security platforms with SIEM/SOC workflows. Lead L3 investigations, automated containment, cloud and Kubernetes security, DevSecOps controls, vendor evaluations, and closed-loop red-team remediation. The role requires automation, infrastructure-as-code security, multi-cloud identity management, incident response, and collaboration with technical leadership.

Provides L2 monitoring, triage, investigation, and escalation for AI and cloud security detections. Analyzes telemetry using KQL or SQL, maintains dashboards, and produces operational, compliance, and risk reporting. Investigates AI threats including prompt injection, jailbreaks, data leakage, agent hijacking, and unauthorized tool activity. Monitors Azure, AWS, and GCP posture, supports vulnerability and compliance remediation, documents cases, and partners with SOC, cloud, incident response, and engineering teams.