About Springer Nature Group
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 180 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. Visit group.springernature.com and follow @SpringerNature / @SpringerNatureGroup
Job Title: MLOps Engineer
Department: Springer Nature AI Labs
Location: Groningen and Pune
Company: Springer Nature
Who we are
At Springer Nature AI Labs (SNAIL), we’re shaping the future of scientific publishing through responsible, human-centred 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.
Who you are
You are an experienced MLOps engineer who loves turning prototypes into reliable, scalable AI systems in the cloud. You balance speed with robustness, automate everything you can, and care deeply about reproducibility, observability and cost efficiency. You are comfortable in a fast-moving environment and enjoy solving complex infrastructure problems. As an experienced engineer, you are happy to mentor junior teammates while continuously improving yourself in this fast-paced field. You thrive in a culture of proactivity, curiosity, experimentation, and teamwork.
What You’ll Do
Build and operate end-to-end ML/LLM pipelines: data ingestion, feature processing, training, evaluation, packaging, registry and deployment.
Automate workflows: design fault-tolerant training/inference pipelines with Kubeflow; implement CI/CD for ML with GitHub Actions and reusable templates.
Serve models: containerize ML models (Docker), expose APIs (FastAPI), deployments in Google Cloud Vertex AI and Kubernetes.
Ensure observability and monitoring: implement metrics, logs and traces; set up model/data quality checks, drift detection and alerting
Optimize cloud cost and performance: finetune the usage of compute resources and apply cloud best practices.
Collaborate: work with ML Engineers and Data(Ops) Engineers to deliver quality products; review code and documentation; apply best coding practices for maintainable and reusable code; support junior colleagues to grow the MLOps capabilities.
Contribute to our culture: bring an experimentation mindset, propose improvements, and help us stay current with modern MLOps tooling and practices.
Coach and mentor more junior team members, ensuring that MLOps skills and capabilities are developed at scale
Must-Have Qualifications
Education: BSc or MSc in Math, Physical Sciences, Computer Science, Software Engineering, AI/ML or related field.
Software: Experienced Python knowledge, testing practices, Git/GitHub, GitHub Actions, Docker; experience building APIs with FastAPI or similar.
Cloud: hands-on experience with at least one major provider (GCP/AWS/Azure) and core services (compute, storage, networking, AI platforms).
MLOps: experience with pipeline orchestration (Airflow/Kubeflow), experiment tracking/model registry.
Monitoring: practical experience setting up dashboards and alerts (e.g., Prometheus/Grafana/OpenTelemetry) and ML-specific monitoring for data drift and performance.
Frameworks: PyTorch or TensorFlow in production contexts.
Communication: clear, proactive communicator in English; able to collaborate with diverse stakeholders.
Work mode: open to hybrid work; team typically spends ~2 days/week in the office.
Nice‑to‑Have
LLMOps: prompt and experiment tracking (e.g., Langfuse), evaluation frameworks, guardrails, vector databases (e.g. Pinecone).
Infrastructure as Code and packaging: Terraform / Pulumi
Experience or understanding of deploying in Kubernetes
Inference optimization: model quantization, Triton/vLLM/TensorRT, GPU operators and scheduling.
By joining Springer Nature, you will actively contribute to the development and implementation of AI solutions that drive the future of scientific publishing. As a leader, you will guide your team to innovate and grow, pushing the boundaries of what’s possible in AI. Join us as we pioneer the future of scientific publishing through artificial intelligence.
Internal applicants: We encourage you to speak with your manager once the interview process has started. At the point of offer acceptance, it is required that you inform your manager. If for any reason you’re unable to do so, please contact HR who can provide guidance as required.
At Springer Nature we value the diversity of our teams. We recognize the many benefits of a diverse workforce with equitable opportunities for everyone. We strive for an inclusive workplace that empowers all our colleagues to thrive. Our search for the best talent fully encompasses and embraces these values and principles. Springer Nature was awarded Diversity Team of the Year at the 2022 British Diversity Awards. Find out more about our DEI work here https://group.springernature.com/gp/group/taking-responsibility/diversity-equity-inclusion
For more information about career opportunities in Springer Nature please visit https://careers.springernature.com/
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Top Skills
What We Do
Research and learning are the cornerstones of progress, which is why we open doors to discovery for our communities, enabling millions of researchers, clinicians, educators and other professionals to access, trust and make sense of the latest insights.
Springer Nature is an ambitious and dynamic organisation. For over 180 years our imprints, books, journals, platforms, and technology solutions have been a trusted source of knowledge to our communities. Today, more than ever, we see it as our responsibility to ensure that fundamental knowledge can be found, verified, understood, and used, ensuring that the world continues to make progress, improving and enriching lives and helping to protect our planet for future generations.
Global key facts:
Established for over 180 years
Nearly 10,000 colleagues
200 offices in 45 countries on all continents
World’s largest academic book publisher
Publisher of Nature, the world’s most influential journal
First company to publish more than 1 million Open Access articles
Key brands and imprints:
Springer Nature is home to some of the best-known names in research, health, educational and professional publishing. Every day, around the globe, our brands reach millions of people.
Nature
Springer
BMC
Palgrave Macmillan
Apress
Macmillan Education
Scientific American
Springer Healthcare
Springer Medizin
Research Square
J.B. Metzler
BSL
Adis
