LLM Workflows Analyst

aiOla is a deep tech lab specializing in Conversational, Voice, and Speech AI. We develop cutting-edge ASR and TTS technologies designed for enterprise use, delivering 95%+ accuracy in any language, accent, or environment. Our solutions empower organizations to capture spoken data in real-time, structure it, and turn it into actionable insights.

About The Role

We are seeking a creative and detail-oriented LLM Workflows Analyst to join our AI team. In this role, you’ll be responsible for designing, testing, and optimizing LLM workflows across diverse, real-world use cases.

This is a customer-facing role where you’ll collaborate closely with both internal teams and external stakeholders. Your mission will be to ensure that prompt-based solutions effectively align business needs with product capabilities. As a bridge between technical innovation and user experience, you’ll help customers unlock the full value of AI by crafting precise, impactful interactions with our models.

Requirements:

• B.Sc. in Industrial Engineering and Management, Data Science, or a related field – mandatory
• Strong logical reasoning and problem-solving abilities, with a passion for iterative and creative experimentation
• Basic proficiency in Python and Git, along with familiarity with production development environment
• Excellent communication and interpersonal skills, with the ability to engage directly with customers and translate business needs into effective technical solutions
• 1-2 years of hands-on experience with Large Language Models (LLMs) and a solid understanding of NLP concepts and methodologies
• Fluent in English, both written and spoken
• Experience with tools and frameworks such as the OpenAI API, Hugging Face, LangChain, or similar – an advantage
  
  

• Design and Optimization: Develop, engineer, and refine effective LLM workflows, including prompt engineering, structured outputs, tooling integrations, and more.
• Customer Collaboration: Partner with customers to deeply understand their goals and use cases, and tailor prompt-based solutions to meet their specific needs.
• Technical Guidance: Provide expert support during customer onboarding and throughout solution implementation.
• Cross-Functional Partnership: Work closely with Product, Engineering, and Data Science teams to integrate LLM capabilities seamlessly into end-user products.
• Continuous Improvement: Evaluate and enhance performance based on internal assessments, customer feedback, benchmarks, and real-world scenarios.