Senior NLP Engineer (Large Language Models)

<p>About the Role: what can you expect?</p> <p> </p> <p>We are looking for a new team member with strong expertise in all aspects of generative AI, in particular large language models (LLMs). The office is located close to Amsterdam in Hoofddorp, the Netherlands, and we offer a hybrid setup with limited home office possibilities.</p> <p> </p> <p>The ideal candidate has an excellent track record on using their skills to support and enhance business processes and outcome. They are strong in communication and eager to extend their competencies, both technical and regarding collaboration with colleagues and clients across the company.</p> <p> </p> <p>You will be responsible for the development, validation, and performance of the solutions you are working on. Your peers are data scientists and platform engineers, and you will jointly deliver text analytics solutions across the firm. Your focus will be to ensure that Swiss Re's maturity regarding large language model usage remains industry-leading.</p> <p> </p> <p>Key responsibilities:</p> <ul> <li> <p>You will develop LLM-based solutions for information extraction, classification, question answering and more, leveraging prompt tuning or model fine-tuning as appropriate.</p> </li> <li> <p>Together with your colleagues, you will contribute to our broader tech strategy for generative AI, involving LLM Ops and systematic re-use of specific models and platforms.</p> </li> <li> <p>You will closely collaborate and coordinate with (re)insurance professionals, and translate business requirements into technical requirements.</p> </li> </ul> <p> </p> <p>About the Team: Join us!</p> <p> </p> <p>Group Data Services (GDS) support Swiss Re's business functions in generating insights from structured and unstructured data. As part of GDS, the Document Analytics Solutions team builds and owns end-to-end solutions and services around text mining, document analytics, and NLP.</p> <p> </p> <p>About You</p> <p> </p> <ul> <li> <p>Master's or Ph.D. in Computer Science, Artificial Intelligence, or a related field</p> </li> <li> <p>6+ years impactful experience in Al-relevant fields and proven expertise, ideally for 2+ years, in working with LLMs in the 10s of billions of parameters</p> </li> <li> <p>Demonstrated ability to drive data analytics projects from ideation to implementation, producing tangible outcomes</p> </li> <li> <p>Ongoing professional development in machine learning and artificial intelligence domains is expected</p> </li> <li> <p>Experience with testing and evaluation of AI systems for explainability and fairness is a plus</p> </li> <li> <p>Strong communication skills to effectively collaborate with both technical and non-technical stakeholders</p> </li> <li> <p>Ability to handle own workload; self-directed and autonomous</p> </li> <li> <p>Ability and enthusiasm to work in a global and multicultural environment</p> </li> </ul> <p> </p> <p>About Swiss Re</p> <p> </p> <p>Swiss Re is one of the world’s leading providers of reinsurance, insurance and other forms of insurance-based risk transfer, working to make the world more resilient. We anticipate and manage a wide variety of risks, from natural catastrophes and climate change to cybercrime. Combining experience with creative thinking and cutting-edge expertise, we create new opportunities and solutions for our clients. This is possible thanks to the collaboration of more than 14,000 employees across the world.<br/> <br/> Our success depends on our ability to build an inclusive culture encouraging fresh perspectives and innovative thinking. We embrace a workplace where everyone has equal opportunities to thrive and develop professionally regardless of their age, gender, race, ethnicity, gender identity and/or expression, sexual orientation, physical or mental ability, skillset, thought or other characteristics. In our inclusive and flexible environment everyone can bring their authentic selves to work and their passion for sustainability.</p> <p> </p> <p> </p> Keywords:  <br/> Reference Code: 127883  <p> </p> <p> </p>