Job Title: MLOPs Engineer

Location(s): Groningen, the Netherlands 

About Springer Nature 

Springer Nature opens the doors to discovery for researchers, educators, clinicians and other professionals. Every day, around the globe, our imprints, books, journals, platforms and technology solutions reach millions of people. For over 175 years our brands and imprints have been a trusted source of knowledge to these communities and today, more than ever, we see it as our responsibility to ensure that fundamental knowledge can be found, verified, understood and used by our communities - enabling them to improve outcomes, make progress, and benefit the generations that follow. 

About the Us 

At Springer Nature AI Lab (SNAIL), we are shaping the future of scientific publishing through responsible, human-centered AI. Our team is at the forefront of integrating advanced AI technologies to optimize processes and enhance the user experience for researchers and academics worldwide. We value a collaborative work environment where ideas flourish and innovation is encouraged. With our curiosity-driven, impact-first culture, we focus on delivering AI innovation at scale, always with integrity and in close collaboration across functions. Our commitment to long-term growth ensures that our people are nurtured and developed to reach their full potential.  

About the Role 

As a MLOps Engineer within the SN AI Lab, you will join a team of engineers designing, deploying, and scaling innovative AI solutions in cloud environments. You will play a critical role in bridging machine learning development and production operations, ensuring AI systems are reliable, secure, scalable, and aligned with business needs. 

Working in a fast-paced and collaborative environment, you will contribute to the end-to-end delivery of AI products, driving engineering excellence, operational efficiency, and responsible AI practices across the organization. 

Role Responsibilities: 

• Design, build, and maintain scalable MLOps platforms, frameworks, and deployment pipelines that support the reliable delivery of machine learning and generative AI solutions.  

• Develop and integrate cloud-native services, automation workflows, and CI/CD practices to improve operational efficiency, system reliability, and deployment velocity.  

• Implement monitoring, observability, tracing, and performance management capabilities to proactively identify issues and optimize production systems.  

• Drive process optimization initiatives that improve model lifecycle management, operational resilience, governance, and overall team effectiveness.  

• Contribute to data security, compliance, and governance standards by ensuring appropriate controls, monitoring, and responsible management of AI and data assets.  

• Support knowledge sharing, technical storytelling, and documentation to increase transparency, adoption, and understanding of AI capabilities across the organization.  

• Mentor and support junior engineers, helping develop MLOps and AI engineering capabilities across the team.  

• Stay current with emerging technologies, AI engineering practices, and industry trends, identifying opportunities to enhance our platforms and delivery approaches.  

About You:

• A degree in Software Engineering, Computer Science, Artificial Intelligence, or a related technical field.  

• Strong experience developing software solutions using Python and modern engineering practices.  

• Experience with GitHub, Docker, and machine learning frameworks such as PyTorch or TensorFlow.  

• Hands-on experience working with cloud platforms such as Azure, AWS, or GCP.  

• Experience building APIs and services using FastAPI or similar frameworks. 

• Knowledge of CI/CD pipelines, automated testing frameworks, and GitHub Actions.  

• Experience implementing observability, monitoring, and tracing solutions for AI and machine learning applications, including tools such as Langfuse or equivalent platforms.  

• Understanding of machine learning lifecycle management, deployment strategies, and production monitoring.  

• Experience deploying and monitoring AI agents and LLM-based applications is considered an advantage. 

Having a good command of English is important; collaboration is important in our day to day work, so being able to communicate your ideas and understand others' is key.

For all roles in all locations, we offer a competitive, industry-benchmarked salary. 

To find out more about the package provided at each location, please visit: https://group.springernature.com/gp/group/careers/current-opportunities/technology-hub 

Springer Nature Skills associated with this Job Profile include:  

SN-Software Engineering & Systems Integration, SN-Product Development & Delivery, SN-Process & Systems Design, SN-Communicates Effectively, SN-Tech Savvy, SN-Manages Complexity, SN-Process Optimization, SN-Storytelling, SN-Big Data Management, SN-Data Security & Governance.  
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