Senior Data Scientist (m/f/x)

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 testing, deployment, and iteration of AI systems

• Contribute to evolving engineering standards as the company transitions from rapid experimentation to more structured, scalable operations

Collaborate Cross-Functionally

• Work closely with product and engineering teams to integrate AI capabilities into core products

• Translate business problems into technical solutions and clearly communicate trade-offs

Drive Structure in a Fast-Paced Environment

• Help bring stability and structure to AI development processes during a high-growth, scale-up phase

• Balance speed of execution with the need for maintainable, well-documented, and scalable systems

• Take ownership of ambiguous problems and drive them through to practical, production-ready outcomes

Core Experience

• 5+ years of experience in Python, with ~10 years of overall software engineering or data experience (candidates transitioning from backend engineering are welcome)

• Proven experience building and deploying production-grade APIs (preferably with FastAPI; REST/GraphQL experience is a plus)

• Hands-on experience working with large language models (LLMs) and LLMOps, including prompt engineering, fine-tuning, and evaluation (e.g., GPT, Claude)

• Strong experience fine-tuning open-source models (e.g., Hugging Face ecosystem)

• Practical experience designing and working with vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector)