What are the responsibilities and job description for the Agentic AI Engineer position at Elsevier?
Are you a collaborative Agentic AI Engineer looking to work for a mission driven global organization?
About the team: The Elsevier Healthcare Education (EHE) Data and Content Software Engineering team is responsible for building and maintaining scalable content ingestion pipelines that power critical health education products. Our work enables flagship platforms such as Sherpath and HESI, ensuring high-quality, reliable, and timely delivery of educational content to learners and educators worldwide. We focus on developing robust, efficient systems that transform and manage complex data, supporting innovation across Elsevier’s health education ecosystem.
https://evolve.elsevier.com/education/
About the Role: We are seeking a Principal Agentic AI Software Engineer to join our team and play a key role in designing and delivering scalable, high-impact software solutions. In this role, you will lead the development of advanced content ingestion and processing systems, driving architectural decisions and engineering best practices across the team. You will collaborate closely with cross-functional partners, mentor engineers, and contribute to building resilient, high-performance platforms that support mission-critical products. This position offers the opportunity to influence technical strategy, champion innovation, and shape the future of content engineering within Elsevier Health Education.
Requirements.
Agentic AI & Advanced Tooling:
• Experience designing, building, or integrating agentic AI systems (e.g., autonomous workflows, multi-step reasoning agents, AI copilots).
• Hands-on experience with LLMs and orchestration frameworks (e.g., LangChain, OpenAI APIs, or similar).
• Ability to design tool-augmented agents (function calling, retrieval-augmented generation, memory systems, planning/execution loops).
• Experience with prompt engineering, evaluation, and guardrails for production-grade AI systems.
• Understanding of AI system architecture, including latency, cost optimization, observability, and reliability of agent workflows.
• Familiarity with vector databases, embeddings, and retrieval systems.
• Experience integrating AI capabilities into enterprise systems and developer workflows.
• Knowledge of responsible AI practices, including safety, bias mitigation, and governance.
Responsibilities:
- Lead the design and development of agentic AI systems
- Architect and deliver autonomous workflows, multi-step reasoning agents, and AI copilots that solve complex business problems across the organization.
- Define and implement LLM-powered architectures
- Design scalable solutions leveraging large language models, orchestration frameworks (e.g., LangChain, OpenAI APIs), and modular service patterns to enable reusable AI capabilities.
- Build and optimize tool-augmented agents
- Develop agents that effectively utilize function calling, retrieval-augmented generation (RAG), memory systems, and planning/execution loops to perform reliable, context-aware tasks.
- Establish best practices for prompt engineering and evaluation
- Create standardized approaches for prompt design, testing, benchmarking, and continuous improvement to ensure high-quality outputs in production environments.
- Implement production-grade guardrails and safety mechanisms
- Design and enforce controls for hallucination mitigation, output validation, policy compliance, and safe execution of AI-driven workflows.
- Drive AI system performance and reliability
- Optimize latency, throughput, and cost efficiency of AI systems while ensuring high availability, observability, and fault tolerance across agent workflows.
- Design and manage retrieval systems
- Architect solutions using embeddings, vector databases, and hybrid search techniques to enable accurate, scalable knowledge retrieval.
- Integrate AI capabilities into enterprise platforms
- Embed AI services into existing products, APIs, and developer workflows, ensuring seamless interoperability with enterprise systems and data sources.
- Lead technical strategy and cross-functional alignment
- Partner with product, data, and engineering leaders to define AI roadmaps, prioritize initiatives, and align solutions with business objectives.
- Champion responsible AI practices
- Ensure systems adhere to standards for fairness, bias mitigation, transparency, and governance, while meeting regulatory and organizational compliance requirements.
- Mentor and elevate engineering teams
- Provide technical leadership, guide architectural decisions, and mentor engineers in building scalable, maintainable AI systems.
- Continuously evaluate emerging technologies
- Stay at the forefront of advancements in AI/ML, agent frameworks, and tooling, and drive adoption of innovations that create competitive advantage.