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Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.

Scope of the Role

Innodata is building a team of Prompt Engineers to leverage large language models (LLMs) to automate and optimize data annotation and human evaluation workflows. In this role, you will design and implement effective prompt strategies that improve accuracy, localization, and cultural alignment in data labeling and translation processes.

Working closely with Product, Data Science, Operations, and client stakeholders, you will translate business requirements into scalable AI-driven solutions. You will identify automation opportunities, develop prompt-based workflows, and continuously measure and refine performance to ensure high standards of quality and reliability.

This position offers the opportunity to directly impact efficiency, scalability and innovation for a leading global technology partner.

What You’ll Own

• Collaborate with data scientists, linguists, and localization experts to ensure accuracy and cultural relevance.
• Prototype and validate AI models to demonstrate initial feasibility, potential impact, and overall effectiveness.
• Design, develop, and implement prompts for data labeling and localization processes within software applications.
• Understand the current components of the software stack, use cases and problems and iterate on solutions leveraging a solid knowledge of data structures, data formats, and data modeling.
• Conduct user testing and feedback analysis to optimize prompt design for data accuracy and linguistic consistency.
• Analyze model performance using key performance indicators (KPIs) and metrics, ensuring that AI models meet customer acceptance criteria and deliver high-quality outputs.
• Communicate technical findings and solution strategies to both technical and non-technical stakeholders, including presenting model performance and actionable insights in a clear, accessible manner.
• Collaborate on data pipelines and workflows that integrate LLMs into automated systems, enhancing both the efficiency and effectiveness of data annotation tasks.
• Create guidelines and training materials for prompt usage in data labeling and localization projects.