LLM Engineer ( large language model)

As an LLM Engineer at our company, your responsibilities will include developing AI-driven solutions focused on real-world applications, transforming prototypes into functional products, and optimizing AI models for performance. You'll become an integral part of cross-functional teams, collaborating in Agile development cycles to create cutting-edge tools that deliver measurable impact, particularly for NVM members in the real estate sector.

To qualify for the LLM Engineer role at our company, you should ideally have proven experience in LLM application development and cloud deployment, preferably using AWS. Familiarity with Retrieval-Augmented Generation (RAG), prompt engineering, and machine learning frameworks such as TensorFlow and PyTorch is essential. Proficiency in Python, along with experience in CI/CD pipelines and ML Ops tools, will also enhance your candidacy.

While a background in real estate is preferred for the LLM Engineer role at our company, it is not a strict requirement. However, having insight into the industry can definitely enhance your capability to understand the challenges faced by our users and create more impactful AI solutions for them.

Yes, our company offers a flexible hybrid work model for LLM Engineers, allowing you to work three days in the office and two days remotely. We also provide a daily remote work allowance to support your home-office setup, making it easier to balance your work and personal life.

As an LLM Engineer at our company, you will have access to a generous professional development budget of €1,500 annually. This can be used for training, workshops, or courses that help expand your skills and knowledge in AI and LLM technologies, ensuring you stay at the forefront of the industry.

In answering this question, focus on a specific project where you implemented an LLM. Discuss your role, the technologies used, the challenges you faced, and the solutions you applied. Highlight the impact of your work, particularly any measurable outcomes or improvements.

Demonstrate your familiarity with cloud platforms, particularly AWS. Share specific examples of projects involving cloud deployment, including how you managed resources and maintained scalability and security. Mention any specific tools or practices you used during deployment.

Talk about the performance metrics you employ to evaluate LLM outputs. Mention processes like A/B testing, user feedback, and continuous monitoring. Emphasize the importance of iterating on outputs based on these evaluations and how this helps to refine the AI solutions.

Explain your approach to prompt engineering, perhaps by discussing how you create effective prompts to elicit the desired response from LLMs. Provide examples of prompts you have crafted in the past and their results, emphasizing the testing process you undertake to perfect them.

Convey your commitment to continuous learning. Mention resources you follow, such as research papers, industry blogs, or conferences. Talk about any relevant certifications or training that you participate in to stay ahead in the rapidly evolving field of LLM technology.

Share specific instances where you've utilized TensorFlow or PyTorch in your projects. Discuss the types of models you've implemented, any customizations you've made, and how you've leveraged these frameworks for optimal performance in your applications.

Highlight your experience working with cross-functional teams and how collaboration has enhanced project outcomes. Discuss your preferred communication strategies and tools for ensuring successful teamwork, especially in Agile environments.

Choose a significant challenge you faced, ideally related to model performance or deployment. Describe the situation clearly, your analysis of the issue, and the steps you took to resolve it. Focus on the lessons learned and how it improved your skills.

Define RAG succinctly and explain its significance in enhancing the capabilities of LLMs. Share any experiences where you've implemented RAG methods in your projects, emphasizing how they improved the efficiency or accuracy of your applications.

Discuss your strategy for optimization, covering aspects like hyperparameter tuning, adjusting model architectures, or leveraging transfer learning. Include examples demonstrating successful optimizations and how they positively impacted model performance.