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Springer Healthcare
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MLOps Engineer

MLOps Engineer

Springer Healthcare

3 - 6 years

6 - 10 Lacs

pune

Posted:1 day ago| Platform:

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Skills Required

github publishing networking coding cloud packaging scheduling data quality monitoring python

Work Mode

Work from Office

Job Type

Full Time

Job Description

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 ML Ops Engineer

D epartment Springer Nature AI Labs

Location Groninge n 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 a n 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 model s (Docker), expose APIs ( FastAPI ), deployments in Google Cloud Ve rtex 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 doc umentation ; 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

M ust-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/ K ubeflow), experiment tracking/model registry

Monitoring practical experience setting up dashboards and alerts (eg 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 (eg Langfuse ), evaluation frameworks, guardrails, vector databases ( eg 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

For more information about career opportunities in Springer Nature please visit

#LI-AR1

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MLOps Engineer