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.
- Stay informed on data labeling and localization industry trends and tools to enhance prompt engineering techniques.
You'll Thrive in This Role If You Have:- 2 years of prompt engineering / LLM fine-tuning, or related AI/ML roles.
- Familiarity with tools/platforms for annotation and human-in-the-loop workflows (e.g., Labelbox).
- Experience designing and automating data annotation workflows.
- Knowledge of data annotation and the challenges of scaling human-in-the-loop workflows.
- Familiarity with cloud platforms, containerization, and model deployment.
- Deep understanding of LLMs (e.g. transformer-based architectures).
- Demonstrated experience programmatically using LLMs to automate data labeling, classification, localization and annotation tasks.
- Strong expertise in Python for NLU, for data processing & transformation, and for statistical analysis. Familiarity with JSON, Javascript or XML.
- Experience with popular frameworks and libraries, including TensorFlow, PyTorch, Jupyter, and other relevant AI/ML tools.
- Familiarity with APIs and platforms for working with LLMs (e.g., OpenAI, Hugging Face, etc.).
- Knowledge of localization best practices and cultural nuances for different languages and regions.
- Strong understanding of LLM evaluation metrics and the ability to assess model reliability, bias, and generalizability.
- Experience working with data pipelines, automation tools, and integrating models into production systems to ensure scalable, reliable solutions.
The expected salary range for this position is $80,000 - $85,000 CAD per year, based on experience, skills, and qualifications.
