About Us
Emerging Technology works on building innovative solutions to provide a hassle-free environment for all our researchers. Along with researchers, we also help internal Springer Nature teams in integrating MLOps/DevOps solutions into their products for an easy-going experience. Our task here is to understand the pain points of our customers and come up with the most innovative, cost-effective, and scalable solutions. Our team is responsible for staying up to date with the latest technology trends in the field of DevOps and MLOps. We conduct experiments to validate their implications and applications at Springer Nature.
About The Role
The purpose of the MLOps/DevOps Engineer role at Springer Nature is to bridge the gap between data science and IT operations. This role focuses on automating and optimizing the processes involved in the deployment, monitoring, and management of machine learning models and AI solutions. Key responsibilities include developing and maintaining CI/CD pipelines for ML models, ensuring system scalability and reliability, enhancing model performance, and driving the operationalization of AI solutions. By staying updated on MLOps and DevOps trends, the MLOps/DevOps Engineer supports continuous improvement and innovation, contributing to Springer Nature's mission of advancing research communication and academic excellence.
Key Responsibilities
- Develop and maintain CI/CD pipelines for AI/ML models, ensuring seamless integration and deployment.
- Automate data collection, preprocessing, and feature engineering processes.
- Implement and manage AI infrastructure, including data pipelines, model deployment, and monitoring systems.
- Collaborate with data scientists and software engineers to ensure models are scalable and reliable for production environments.
- Optimize model performance and scalability for production use.
- Research and experiment with new MLOps and DevOps tools to drive innovation.
- Communicate technical details and operational insights to non-technical stakeholders through clear and effective documentation.
- Apply MLOps practices to ensure model reproducibility, transparency, and compliance.
- Monitor and maintain deployed models, including retraining and updating them as needed.
- Participate in incident management and post-mortem processes to continuously improve system reliability.
What You Will Be Doing
Within 3 Months you will:
- Get familiar with Springer Nature's technology stack, including AI/ML frameworks and cloud platforms (AWS, Azure, or Google Cloud).
- Begin developing and maintaining CI/CD pipelines for AI/ML models under the guidance of senior team members.
- Participate in team agile processes and ceremonies, including daily stand-ups, planning, and retrospectives.
- Collaborate with data scientists and software engineers to understand model requirements and deployment processes.
- Share insights and opinions on building scalable and reliable AI/ML solutions.
By 3-6 Months You Will
- Become an active contributor to the development and maintenance of AI/ML infrastructure, focusing on automating and optimizing operational processes.
- Help improve AI infrastructure, including data pipelines and model deployment systems.
- Develop a solid understanding of Springer Nature's editorial processes and how AI/ML solutions can enhance operational efficiency.
- Engage in technical discussions with the team to improve product architecture and code quality.
- Communicate findings and insights to non-technical stakeholders through documentation and storytelling.
By 6-12 Months You Will
- Lead the development and deployment of robust CI/CD pipelines and ensure the ongoing performance and scalability of AI/ML models.
- Research and experiment with new MLOps and DevOps technologies to drive innovation within the team.
- Onboard new team members and support their integration into the team's agile processes.
- Participate in blameless post-mortems to identify and implement improvements.
- Proactively provide feedback and coaching to junior members of the team.
- Advocate for defining and implementing non-functional requirements and influence the design of the system architecture.
- Engage in user research to better understand the needs of researchers and other users of Springer Nature's platforms.
About You
- Bachelor's or master's degree in computer science, Engineering, or a related field.
- 3+ years of experience in MLOps, DevOps, or related engineering roles, with a strong understanding of CI/CD practices and cloud infrastructure.
- Proficiency in programming languages such as Python, Shell, R, or Java.
- Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Strong understanding of MLOps/DevOps concepts and tools (e.g., Docker, Kubernetes, Jenkins).
- Experience with cloud platforms such as AWS, Azure, or Google Cloud for deploying AI/ML solutions.
- Excellent problem-solving and analytical skills.
- Effective communication and teamwork skills.
- Experience in automating and optimizing machine learning workflows.
- Knowledge of monitoring and logging tools to ensure system reliability (e.g., Prometheus, Grafana, ELK Stack).
Join our team to make a significant impact on the future of research communication and academic excellence at Springer Nature.
