Senior LLM Architect

As a Senior LLM Architect at Weekday's client, you'll focus on designing robust architectures for integrating large language models into the platform. Your responsibilities will include optimizing model performance for specific use cases, deploying high-availability systems, and collaborating with engineering and product teams to align solutions with business needs. You'll also manage data pipelines and mentor engineers on best practices in AI architectures.

To qualify for the Senior LLM Architect role at Weekday's client, candidates should have over 5 years of experience in AI/ML, with a specific emphasis on NLP and LLMs. You should also possess at least 2 years in leadership or architectural roles. Proficiency in Python and familiarity with frameworks like PyTorch or TensorFlow are essential, alongside a deep understanding of transformer-based architectures and cloud platforms.

A Senior LLM Architect at Weekday's client should have a strong command of transformer architectures like GPT and BERT, as well as hands-on experience with NLP libraries such as Hugging Face. Knowledge of distributed systems, MLOps processes, and cloud platforms like AWS, GCP, or Azure is crucial, enabling you to navigate the complexities of scaling AI solutions.

A Senior LLM Architect plays a critical role in fostering collaboration within the team at Weekday's client. By guiding technical initiatives and mentoring engineers, you'll ensure best practices are followed, driving innovation and efficiency in AI solutions. Your leadership helps align AI strategies with the company's long-term business objectives, ensuring the team remains at the forefront of technology.

In the role of Senior LLM Architect at Weekday's client, you can look forward to engaging in projects that involve integrating state-of-the-art language models into diverse applications. You'll tackle unique challenges in optimizing performance, deploying AI systems in production, and innovating with new NLP techniques—contributing to the future of AI-powered solutions.

When asked about your experience with transformer-based architectures, be specific about the models you've worked with, such as GPT or BERT. Discuss the projects where you've implemented these models, the challenges you faced, and how you overcame them. Highlight any optimizations you made to enhance performance.

In your response, outline techniques such as fine-tuning models on specific datasets, implementing techniques like transfer learning, and utilizing hyperparameter tuning. Explain how these methods improve model accuracy and efficiency, showcasing your understanding of AI model optimization.

Discuss your approach to fostering communication and collaboration with different teams, emphasizing the importance of aligning technical solutions with business objectives. Provide examples of successful projects where you collaborated with backend engineers or product managers, highlighting your interpersonal skills.

When answering, detail your experience in designing and optimizing data pipelines for training and inference workflows. Mention any specific tools or frameworks you've used and discuss how effective data management contributes to LLM performance and reliability.

Share a specific example of a challenge you encountered—like latency issues or scaling challenges—while deploying an LLM. Explain your thought process, the strategies you employed to overcome the challenge, and the impact of your resolution on the project.

Discuss your strategy for staying informed on the latest advancements in NLP, such as following research publications, attending conferences, and participating in online communities. Describe how you apply new knowledge to improve your work and lead your team effectively.

Be sure to mention your proficiency in Python and specific frameworks like TensorFlow or PyTorch. Provide examples of projects where you utilized these tools, emphasizing your coding skills and ability to leverage AI libraries effectively to achieve your goals.

When asked, be ready to describe a specific project where you played a leading role. Outline the project goals, your approach to solving problems, the technologies used, and the final outcomes, highlighting how your leadership contributed to its success.

In your response, discuss your philosophy on mentorship and your methods for guiding junior engineers. Explain how you encourage their growth, share knowledge, and foster a collaborative team environment to enhance their professional development.

Your answer should cover various evaluation metrics such as accuracy, F1 score, and precision/recall. Discuss your process for testing models, interpreting results, and making adjustments based on feedback and data analysis to improve model effectiveness.