Senior LLM Architect

This role is for one of the Weekday's clients

We are looking for an LLM Architect to design, implement, and scale advanced architectures that seamlessly integrate large language models (LLMs) into our platform. In this role, you will drive the technical strategy for optimizing, deploying, and innovating LLM-based solutions, ensuring they align with product goals. This is an exciting opportunity to work at the cutting edge of AI and NLP, shaping the future of AI-powered work solutions.

Requirements

Key Responsibilities:

• Architectural Design: Develop scalable and robust architectures to integrate LLMs into our platform.
• LLM Optimization: Fine-tune and enhance model performance, efficiency, and reliability for product-specific use cases.
• Scalable Deployment: Design and implement low-latency, high-availability systems for deploying LLMs in production.
• Cross-Functional Collaboration: Partner with backend engineers, AI/ML teams, and product managers to align technical solutions with business goals.
• Innovation & Strategy: Drive the adoption of state-of-the-art NLP techniques, keeping our platform at the forefront of AI technology.
• Data Pipeline Management: Oversee the design and optimization of data pipelines for training and inference workflows.
• Mentorship: Guide and mentor engineers on best practices for working with LLMs and scalable AI architectures.

What We’re Looking For:

• Experience:
• 5+ years in AI/ML, with a strong focus on NLP and LLMs.
• 2+ years in technical leadership or architectural roles.
• Technical Expertise:
• Deep understanding of transformer-based architectures (GPT, BERT, LLaMA).
• Proficiency in Python and frameworks like PyTorch or TensorFlow.
• Hands-on experience with Hugging Face, LangChain, or similar NLP libraries.
• Knowledge of distributed systems, MLOps, and cloud platforms (AWS, GCP, Azure).
• Problem-Solving Skills: Ability to tackle complex technical challenges and optimize large-scale AI systems.
• Data Expertise: Strong experience handling large datasets for training and inference.
• Leadership Skills: Capable of leading technical initiatives, mentoring engineers, and aligning AI strategies with long-term business objectives.
• Research Mindset: Passion for staying ahead of NLP advancements and applying them to real-world AI solutions.