Data Scientist (m/f/x)
Location: EU
(Remote)
Who You’ll Work For:
At Makersite , we're pioneering the future of sustainable product development and digital collaboration. As a leading platform for product lifecycle management (PLM), we empower companies to make smarter, more sustainable decisions across their entire supply chain. Our cutting-edge software enables teams to design, prototype, and manufacture with transparency, efficiency, and responsibility—reducing environmental impact while optimizing performance.
We're a fast-growing, innovative company that thrives on creativity, collaboration, and continuous learning. If you're passionate about technology, sustainability, and creating meaningful impact, we’d love to hear from you. Join us and be a part of shaping the future of manufacturing and product innovation.
Who we're looking for:
As part of our growing team, you will play a key role in shaping the future of sustainable product development. We’re looking for innovative, driven Data Scientists who are passionate about technology and sustainability to join us in building tools that enable companies to make smarter, more responsible decisions. In this role, you’ll have the opportunity to collaborate with talented professionals, contribute to cutting-edge projects, and help drive the digital transformation of industries worldwide. Ifyou're ready to make an impact and contribute to meaningful change, we want to hear from you!
This role is a fixed, permanent position. All successful applicants will receive a permanent employment contract regardless of location.
The role:
Develop and Deploy Production-Grade AI Systems
Build, deploy, and maintain scalable APIs that serve AI/ML models in production environments
Own end-to-end delivery of AI solutions, from prototyping to fully productionized systems
Design Advanced AI Architectures
Design and implement Retrieval-Augmented Generation (RAG) systems using vector databases
Build and orchestrate AI agents using frameworks such as LangGraph, CrewAI, or similar
Evaluate and select appropriate large language models (LLMs) and foundation models based on specific use cases
Optimize Performance and Scalability
Continuously optimize model inference for latency, cost efficiency, and throughput at scale
Identify bottlenecks and implement improvements to ensure high-performing systems
Ensure Reliability and Observability
Implement robust monitoring, logging, and alerting for deployed models and services
Ensure system reliability, uptime, and performance through best practices in production ML systems
Establish Engineering Best Practices
Build and maintain CI/CD pipelines for seamless