Top Artificial Intelligence Jobs in the Netherlands

Work on-site with clients to translate ambiguous requirements into production AI systems. Build and launch client-specific agentic workflows, integrate with legacy stacks, manage security/compliance constraints, lead technical client relationships, mentor peers, and feed deployment insights back to platform engineering to improve scalability and adoption.

Design, build, and deploy production AI agents in Python, integrate with enterprise systems, evaluate and maintain agent performance, and collaborate cross-functionally while producing well-tested, documented code and CI/CD pipelines.

Lead global channel strategy for AI-native networking, enabling partners to deliver AI-optimized infrastructure, automation, and security. Define standards for partner specialization, drive partner readiness and investments, represent HPE at partner and industry events, and align cross-functional teams to scale repeatable AI networking deployments and self-driving network models.

4-6 month full-time internship in a consulting team to identify AI use cases, evaluate and test AI tooling/solutions, and support digital transformation programs for consumer and manufacturing clients. Work includes shaping operating models, data architecture recommendations, and accelerating delivery through AI-driven processes while collaborating with consultants and industry experts.

Lead research and engineering for large-scale LLM and multi-modal pre-training: design architectures and tokenizers, curate massive datasets, run distributed training on thousands of GPUs, debug performance bottlenecks, and improve cross-modal alignment and efficiency.

Contract AI evaluator (Dutch) who tests and rates chatbot and language model responses via structured written and voice interactions. Submit recordings, ratings, and write-ups per task SOW. Flexible, task-based work; must pass language and technical assessments and provide your own devices and internet.

Build and ship full-stack AI products end-to-end: low-level systems, backend infrastructure, and polished UIs. Collaborate with founders and product teams, iterate quickly, own production outcomes, mentor engineers, and solve distributed systems, RAG/agent, and cloud-native challenges.

As an AI Product Manager, you will define the product roadmap, collaborate with teams to develop AI features, conduct market research, and ensure ethical AI use.

Seeking an AI/ML Engineer to develop and optimize machine learning models for mobile apps, collaborating with teams to enhance user experiences.

As an AI Automations Engineer, you will design and build tooling for GTM, Operations, and Finance teams, automating processes and enhancing systems with a focus on AI.

Build production AI-native systems for BJAK's AI Neobank: AI agents, LLM apps, RAG systems, tool-calling, and automation to improve onboarding, KYC, support, risk review, CRM, and operations. Collaborate with product and engineering teams to ship, evaluate, and operate reliable, secure, cost-effective AI workflows that minimize hallucination and scale manual processes into dependable systems.

As an AI Engineer, you'll design and deploy production-ready AI/ML systems, optimizing pipelines and collaborating with teams to generate measurable business value in Climate Tech.

Build, integrate, and govern internal AI platforms for IT Operations. Develop API integrations, automations, ITSM workflows, AI governance tooling, and cross-functional AI use-cases while ensuring security, GDPR compliance, and driving adoption across the organisation.

Lead EMEA go-to-market for Physical AI by sourcing executive-level pipeline, accelerating top accounts, building EMEA-specific playbooks, enabling regional sales, and converting lighthouse wins into sustained cloud consumption across robotics, autonomy, simulation, and related AI-native industries.

Own global TA operations, tech stack, data and workflows; build dashboards and automations; run interviewer enablement; partner on AI pilots and governance; ship low-code tools and integrate AI into recruiting processes to improve time-to-fill and recruiter productivity.

Design, develop, and deploy AI-powered enterprise applications using Python, LLMs, RAG, and vector/semantic search. Build scalable backend services and RESTful APIs, handle structured and semi-structured data, perform data analysis and validation, collaborate with stakeholders, ensure governance/security, and participate in Agile delivery and continuous improvement.

Design, build, and deploy scalable AI/ML and Generative AI solutions for enterprise systems. Develop Python-based production applications, APIs, microservices, and MLOps/CI-CD pipelines. Implement data ingestion, preprocessing, feature engineering, model training/validation, deployment, monitoring, and lifecycle management. Collaborate with DevOps, data engineering, and product teams to ensure secure, compliant, and high-performance AI solutions.

Technical Business Analyst responsible for gathering, documenting, and translating business and technical requirements for Azure DevOps, CI/CD, AI, and platform engineering initiatives. Manage Azure DevOps Boards, define user stories and acceptance criteria, support Agile delivery, collaborate with DevOps, AI, and platform teams, and drive automation, release readiness, and end-to-end requirement traceability.

Lead enterprise adoption of GitHub Copilot and AI-assisted developer tools, providing hands-on guidance, troubleshooting integrations, defining best practices, running enablement sessions, measuring adoption and productivity, and aligning implementations with security, governance, and DevOps practices.

Lead technical delivery and architecture of enterprise ServiceNow solutions across ITSM, CSM, HRSD, and CMDB. Design Agentic AI (MCP) solutions, integrations (REST, MID Server, IntegrationHub), server-side scripting (Glide Script), workflows (Flow Designer), and automated tests (ATF). Provide technical leadership, run discovery workshops, perform architecture/code reviews, mentor consultants, and resolve complex delivery challenges.

Lead and build Axelera's AI Infrastructure Systems division to deliver datacenter-class hardware from accelerator cards and OAM/UBB modules to 1U/2U racks. Own architecture, schematics, PCB/layout review, power/thermal/mechanical design, BMC/firmware, bring-up, validation, qualification, NPI, and ramp with CMs/ODMs. Align roadmaps with silicon, SDK, and product teams, engage customers and partners, set engineering processes, manage budget and vendor relationships, and mitigate technical risks through hands-on leadership.

Lead design, training, and evaluation of LLMs for translation and language-quality tasks; build robust evaluation frameworks; architect agentic multi-model workflows; own model development from data curation to production; run reproducible experiments; collaborate with product and engineering and mentor researchers.

Own the end-to-end process converting product, marketing, and account signals into qualified sales opportunities. Build qualification criteria, routing rules, data enrichment and automation, partner across product/marketing/sales/analytics, create feedback loops, and measure and optimize funnel conversion and data quality.

Lead design and delivery of AI-powered features for CLion and C++ workflows. Architect AI integrations and agent tools, prototype and validate debugging, code-understanding, refactoring, and navigation workflows, build benchmarks and evaluation frameworks, and collaborate with product, ML, QA, and engineering teams to set technical direction and roadmap for AI-assisted C++ development.

Own acquisition, onboarding, and activation for JetBrains AI. Improve product entry points, trials, demos, and onboarding flows. Lead experiments, analyze funnels and cohorts, remove friction, and partner with design, engineering, analytics, marketing, and monetization to drive activation and conversion.