Senior Software Engineer I – Search and AI Platform
About the Team
Our team is dedicated to unlocking the rich knowledge embedded within Elsevier’s content through our rich data platform; this empowers researchers, clinicians, and innovators worldwide to gain new insights, make informed decisions, and accelerate progress across research, healthcare, and life sciences.
As part of this mission, the AI Platform team is focused on building reusable generative AI capabilities that can be embedded across products and platforms. We create scalable, secure, and production-ready AI services — enabling downstream teams to integrate cutting-edge GenAI features with confidence.
The Role
We are seeking a Senior Software Engineer I with a passion for generative AI and platform architecture. In this role, you will drive architectural decision and set engineering standard and implement robust systems to deliver reusable AI services and components, collaborating closely with cross-functional stakeholders to solve meaningful challenges. You will also contribute to mentor other engineers and help evolve our engineering practices.
Responsibilities
- Leading architectural design and ensure technical consistency.
- Designing, developing, and maintain generative AI services and reusable components using mostly Python and a little bit Java.
- Defining and promote best practices in engineering, including scalability, observability, testing, and CI/CD.
- Contributing to system designs spanning multiple services and modules, aligning with architectural best practices.
- Collaborating with product, platform, and research teams to translate AI prototypes into production-ready capabilities.
- Working within a Kubernetes (EKS) environment to deploy scalable, containerized applications.
- Leading the resolution of complex technical challenges across distributed systems.
- Mentoring less-senior developers on engineering principles, GenAI patterns, and platform development.
- Participating in code reviews, architecture sessions, and cross-team initiatives to ensure quality and maintainability.
- Staying informed of the latest developments in generative AI and advocate for responsible integration into product ecosystems.
What We’re Looking For
- 4-6 years of software engineering experience.
- Proven experience contributing to technical architecture for large-scale platforms or services.
- Solid understanding of software development methodologies and data modeling principles.
- Deep expertise in Python and Java.
- Proficiency in backend development and familiarity with modern AI/LLM tools and frameworks (e.g., LangChain, LangGraph).
- Experience with Kubernetes (EKS) and cloud-native architectures.
- Proven track record building scalable backend systems and APIs.
- Experience mentoring engineers and contributing to architectural decisions.
- Ability to work collaboratively across functions in an Agile or Kanban environment.
Nice to Have
- Experience operationalizing LLMs or building internal AI platforms.
- Familiarity with observability practices (metrics, logging, alerts).
- Exposure to knowledge graphs or semantic search systems.
U.S. National Base Pay Range: $86,600 - $144,400. Geographic differentials may apply in some locations to better reflect local market rates. If performed in Maryland, the base pay range is $90,900 - $151,700.If performed in New Jersey, the base pay range is $102,333 - $163,467. This job is eligible for an annual incentive bonus.
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Skills Required
- 4-6 years of software engineering experience
- Experience contributing to technical architecture for large-scale platforms or services
- Understanding of software development methodologies and data modeling principles
- Deep expertise in Python and Java
- Backend development experience and familiarity with modern AI/LLM tools and frameworks, such as LangChain and LangGraph
- Experience with Kubernetes, Amazon EKS, and cloud-native architectures
- Track record building scalable backend systems and APIs
- Experience mentoring engineers and contributing to architectural decisions
- Ability to collaborate across functions in an Agile or Kanban environment
- Experience operationalizing LLMs or building internal AI platforms
- Familiarity with observability practices, including metrics, logging, and alerts
- Exposure to knowledge graphs or semantic search systems
Elsevier Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Elsevier and has not been reviewed or approved by Elsevier.
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Leave & Time Off Breadth — Feedback suggests paid time off spans vacation, holidays, sick days, bereavement, military leave, and volunteer time. Family-related leave options are also emphasized as part of the package.
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Healthcare Strength — Feedback suggests medical, dental, vision, life insurance, wellness initiatives, and an EAP form a comprehensive health offering. Gym support and wellbeing hubs reinforce an ongoing health and wellness focus.
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Retirement Support — Feedback suggests retirement programs include a 401(k)/retirement plan and pension options, with long-term savings vehicles such as an employee stock purchase plan also available. These elements contribute to a sense of financial security beyond base pay.
Elsevier Insights
What We Do
Elsevier is a world-leading provider of information solutions that enhance the performance of science, health, and technology professionals, empowering them to make better decisions, and deliver better care. Because informed decisions lead to better outcomes, Elsevier is a leader in information and analytics for customers across the global research and health ecosystems. Elsevier helps researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. We do this by facilitating insights and critical decision-making for customers across the global research and health ecosystems.
