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About the role
- Prompt Engineer designing LLM workflows for Innodata, a global data engineering company. Automating data annotation, localization, and human-evaluation processes.
Responsibilities
- Design and implement prompt strategies to improve accuracy, localization, and cultural alignment in data labeling and translation processes
- Translate business requirements into scalable AI-driven solutions with Product, Data Science, Operations, and client stakeholders
- Identify automation opportunities and develop prompt-based workflows
- Continuously measure and refine performance to ensure quality and reliability
- Collaborate with data scientists, linguists, and localization experts
- Prototype and validate AI models
- Design, develop, and implement prompts for data labeling and localization within software applications
- Understand software stack components, use cases, data structures, data formats, and data modeling to iterate on solutions
- Conduct user testing and feedback analysis to optimize prompt design
- Analyze model performance using KPIs and metrics against customer acceptance criteria
- Communicate technical findings and solution strategies to technical and non-technical stakeholders
- Collaborate on data pipelines and workflows integrating LLMs into automated systems
- Create guidelines and training materials for prompt usage
- Monitor industry trends and tools in data labeling and localization
Requirements
- 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, including transformer-based architectures
- Demonstrated experience programmatically using LLMs to automate data labeling, classification, localization and annotation tasks
- Strong expertise in Python for NLU, data processing and transformation, and statistical analysis
- Familiarity with JSON, Javascript or XML
- Experience with TensorFlow, PyTorch, Jupyter, and other relevant AI/ML tools
- Familiarity with APIs and platforms for working with LLMs, such as OpenAI and Hugging Face
- Knowledge of localization best practices and cultural nuances for different languages and regions
- Strong understanding of LLM evaluation metrics and ability to assess model reliability, bias, and generalizability
- Experience working with data pipelines, automation tools, and integrating models into production systems
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