Springer Nature - Senior Artificial Intelligence/Machine Learning Engineer - Python/LLM

Note : Women Candidate Preferred

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.

Job Title : Senior AI ML Engineer

Location : Pune, India

About Us :

Springer Nature AI labs work on building innovative solutions to accelerate discovery and scientific progress for the research community. Along with researchers, we also help internal Springer Nature teams in integrating AI solutions in their products for a state-of-the-art experience. Our task here is to understand the pain points of our customers, develop problem statements together with them and come up with the most innovative, cost effective and scalable solution. Our team is responsible for staying up to date with the latest technology trends in the field of AI and GenAI. We conduct experiments to validate their implications and applications at Springer Nature.

About the Role :

The purpose of the AIML Engineer role at Springer Nature is to enhance the publishing cycle using advanced AI and ML skills. This role focuses on improving operational efficiency and decision-making by developing and deploying AI/ML solutions to streamline processes, improve data accuracy, and enable new capabilities.

Key responsibilities include staying updated on AI/ML trends, ensuring system scalability and reliability, improving data quality, detailed data analysis, enhancing user experience, and driving business insights.

Key Responsibilities :

- Design, develop, and deploy end-to-end AI/ML solutions, ensuring scalability, efficiency, and robustness.

- Lead AI/ML initiatives, collaborating with cross-functional teams including data scientists, software engineers, and product managers.

- Architect and optimize AI infrastructure, including data pipelines, model training workflows, and deployment systems.

- Evaluate, benchmark, and fine-tune AI/ML models to ensure optimal performance in production environments.

- Implement and enforce best practices for model monitoring, retraining, and maintenance.

- Drive innovation by researching, prototyping, and implementing cutting-edge AI technologies, including Generative AI and Large Language Models (LLMs).

- Provide technical mentorship and guidance to junior AI/ML engineers and contribute to their professional growth.

- Collaborate with non-technical stakeholders to translate business challenges into AI/ML solutions and communicate results effectively.

- Develop and contribute to AI governance frameworks, ensuring ethical and responsible AI practices.

- Lead initiatives for integrating AI/ML models into existing and new Springer Nature platforms to enhance user experiences.

Within 3 Months :

- Gain a deep understanding of Springer Natures AI/ML ecosystem, including technology stack, data infrastructure, and cloud platforms (Google Cloud).

- Take ownership of small-to-medium AI/ML projects, collaborating with team members to design, implement, and deploy models.

- Participate in architecture discussions and contribute to technical design decisions for AI solutions.

- Review existing models, identify potential optimizations, and propose improvements.

- Establish best practices for model deployment, testing, and monitoring.

- Work closely with data scientists to enhance feature engineering and improve model performance.

- Contribute to documentation, knowledge sharing, and internal technical discussions.

By 3-6 Months :

- Lead the design and development of AI/ML solutions, ensuring high scalability and performance.

- Optimize AI/ML model inference and deployment pipelines to meet production requirements.

- Implement monitoring and alerting mechanisms for deployed AI models to track performance degradation and ensure timely retraining.

- Engage with business and product teams to identify AI-driven opportunities that enhance editorial workflows and customer experiences.

- Conduct deep-dive research into new AI methodologies, including model compression, fine-tuning strategies, and RAG applications.

- Mentor junior AI/ML engineers, providing technical guidance and conducting code reviews.

- Drive improvements in AI infrastructure, including automation, CI/CD pipelines, and GPU resource management

By 6-12 Months :

- Own and drive multiple AI/ML projects from conception to deployment, ensuring alignment with business objectives.

- Lead AI research initiatives, evaluating the latest advancements and integrating state-of-the-art techniques into production models.

- Influence and shape the AI/ML roadmap, identifying opportunities for automation and intelligent decision-making across platforms.

- Advocate for AI ethics, model interpretability, and fairness, ensuring responsible AI development practices.

- Lead collaboration with engineering teams to define and implement AI-driven enhancements to platform features and user experiences.

- Provide training and onboarding for new AI/ML engineers, ensuring seamless integration into the team.

- Represent Springer Nature in AI/ML conferences, research publications, and industry forums to share insights and learn from the community.

About you :

- Bachelor's, Masters, or PhD in Computer Science, Engineering, or a related field.

- 6-9 years of experience in AI/ML engineering, with extensive hands-on experience in machine learning, deep learning, and Generative AI.

- Proficiency in programming languages such as Python, R, and expertise in Data Structures and Algorithms.

- Deep understanding of AI/ML concepts, including model training, optimization, and deployment at scale.

- Experience with software engineering best practices, including CI/CD, version control (Git), testing, and containerization (Docker, Kubernetes).

- Strong problem-solving skills and the ability to translate complex AI/ML research into practical solutions.

- Hands-on experience in NLP, Computer Vision, and Large Language Models (LLMs), including developing and fine-tuning RAG applications.

- Experience working with cloud platforms such as AWS, Azure, or Google Cloud for AI/ML deployment and scaling.

- Excellent communication and leadership skills, with a proven ability to mentor junior engineers and collaborate with cross-functional teams.