Text & Reasoning Tech

Role Purpose

Lead the design, development, and delivery of advanced Natural Language Understanding (NLU) solutions leveraging Large Language Models (LLMs) for text and reasoning tasks. Ensure technical excellence, innovation, and alignment with Omilia’s strategic objectives in conversational AI.

Accountabilities

• Technical Correctness: Own the technical integrity and quality of NLU solutions, ensuring robust, scalable, and maintainable code and models.
• System Quality: Oversee architectural decisions, code reviews, and best practices for LLM-based NLU systems.
• Delivery Monitoring: Ensure timely and successful delivery of NLU features and improvements, proactively identifying and mitigating risks.
• Autonomy: Operate with high independence, making critical technical decisions and setting standards for the team.
• Scope & Complexity: Lead initiatives involving complex reasoning, multi-turn dialogue, and advanced text understanding, often spanning multiple projects or products.
• Impact: Drive measurable improvements in NLU accuracy, performance, and customer satisfaction.
• Influence/Mentorship: Mentor engineers and data scientists, fostering a culture of technical excellence and continuous learning.

Key Responsibilities

• Design and implement state-of-the-art LLM-based NLU models for text and reasoning tasks.
• Lead technical discussions, architecture reviews, and solution design sessions.
• Collaborate with cross-functional teams (Product, Data Science, Engineering) to define requirements and deliver solutions.
• Conduct code and model reviews, ensuring adherence to best practices and Omilia standards.
• Monitor system performance, troubleshoot issues, and drive continuous improvement.
• Stay abreast of advancements in LLMs, NLU, and AI, evaluating and integrating relevant technologies.
• Provide technical mentorship and guidance to team members.

Requirements

• Advanced degree in Computer Science, Computational Linguistics, or related field.
• Proven experience in designing, developing, and deploying LLM-based NLU systems.
• Deep understanding of NLP, machine learning, and reasoning algorithms.
• Proficiency in Python and relevant ML frameworks (e.g., PyTorch, TensorFlow, HuggingFace).
• Experience with cloud platforms and scalable architectures.
• Strong problem-solving, communication, and leadership skills.
• Track record of technical mentorship and driving impactful results in AI/NLU projects.