AI Engineer

Arbeitnow; Nederland; Hybrid; EUR 90,000 - 150,000; Full time

Job summary

Arbeitnow is seeking an AI Engineer in the Netherlands to build and deploy production-grade AI systems across heavy industries. You will work with agentic AI, multimodal ML, data from sensors, and industrial software to improve reliability and decision-making in real-world operations.

This hands-on role requires owning end-to-end deployments, including data ingestion, model serving, and integration with ERP/CMMS/WMS platforms, delivering scalable solutions in a fast-paced environment.

Qualifications

• 3+ years of experience building and shipping production AI systems; 5+ years preferred.

Responsibilities

• Develop and deploy agentic AI systems, including orchestration, tool use, state management, and human-in-the-loop workflows.

Skills

Python

TypeScript

Backend development

Frontend development

Production AI

Tools

FastAPI

PostgreSQL

OpenCV

React Native

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI Engineer based in Netherlands.

We are looking for an experienced AI Engineer to build and deploy production-grade AI systems that address complex challenges across heavy industries, including utilities, manufacturing, logistics, and energy. You will play a key role in developing a next-generation industrial operating system, taking innovative products from concept to production. The role combines agentic AI, multimodal machine learning, industrial data integration, and full-stack engineering. You will work with real-world data from sensors, machines, and operational systems to deliver intelligent solutions that improve efficiency, reliability, and decision-making. This is a high-ownership opportunity in a fast-moving, zero-to-one product environment. You will help establish the foundations of an ambitious platform designed to transform industrial operations.

Accountabilities

• Develop and deploy agentic AI systems, including orchestration, tool use, state management, and human-in-the-loop workflows.
• Build agent runtimes and orchestration frameworks supporting specialized agents for vision quality, predictive maintenance, and operations planning.
• Design context assembly systems using industrial ontologies and memory, including tool dispatch, approval gates, tracing, and cost controls.
• Develop multimodal data pipelines combining video processing, computer vision, and structured operational data.
• Implement computer vision capabilities such as object detection, segmentation, and action recognition, using technologies such as YOLO and OpenCV.
• Process industrial sensor data, including RTSP video, time-series telemetry, vibration, thermal data, and PLC/OPC-UA streams.
• Build signal-processing and sensor-correlation workflows, including FFT feature extraction and cross-sensor analysis.
• Design and contribute to industrial ontologies and knowledge graphs representing plants, assets, sensors, work orders, materials, and maintenance histories.
• Develop memory systems for trace storage, playbooks, asset templates, and reusable failure-pattern libraries.
• Build operational interfaces such as dashboards, evidence-based alerting workflows, replanning tools, and audit trails.
• Develop integration connectors for ERP, CMMS, WMS, industrial historians, and PLC systems, ensuring data normalization and consistent schema mapping.
• Deliver end-to-end solutions spanning data ingestion, model serving, backend services, and frontend interfaces.
• Ensure production systems meet demanding requirements for reliability, latency, scalability, cost efficiency, and operational usability.

Requirements

The ideal candidate is an autonomous, hands-on AI engineer with experience taking complex systems from initial development to production, particularly in environments involving physical-world data and industrial operations.

• At least 3 years of experience building and shipping production AI systems; 5+ years preferred.
• Proven experience developing agentic AI products and production systems, including orchestration, tool integration, state management, and human oversight.
• Experience as an early-stage engineer at a venture-backed startup, contributing to products and systems from zero to one.
• Strong background in AI and machine learning applied to physical-world systems.
• Experience working with large-scale industrial data, such as video streams, IoT devices, sensor telemetry, vibration, thermal data, or PLC/OPC-UA systems.
• Hands-on experience building multimodal AI pipelines that combine computer vision with structured operational data.
• Understanding of data ingestion, normalization, temporal alignment, data quality, and ground-truth challenges in real-world environments.
• Experience with industrial ontologies, knowledge graphs, or structured context systems based on asset hierarchies and operational processes.
• Experience integrating AI systems with enterprise and industrial platforms such as ERP, CMMS, WMS, historians, or PLC systems.
• Strong software engineering skills across backend and frontend development, including Python and TypeScript.
• Experience with production-oriented technologies such as FastAPI, PostgreSQL, React Native, and OpenCV.
• A practical understanding of system reliability, failure modes, latency, cost management, and human interaction.
• Ability to work autonomously, take ownership, and deliver in a fast-paced, high-intensity environment.
• Strong motivation to solve complex industrial challenges through hands-on engineering and applied AI.

Benefits

• Opportunity to help build a category-defining industrial operating system with the potential to shape manufacturing plants and industrial facilities over the next 10–20 years.
• Exceptional employee equity package, with an equity pool described as twice the industry standard and an estimated value of more than $200,000 from day one.
• Opportunity to work alongside an experienced team with backgrounds in enterprise technology, industrial robotics, autonomous driving, and academic research.
• Exposure to complex, real-world industrial challenges and large volumes of underutilized sensor data.
• Direct impact on operational efficiency, predictive maintenance, industrial reliability, and decision-making across major industries.
• Opportunity to contribute to a zero-to-one product in an ambitious, innovation-driven environment.