Innodata Inc.
(NASDAQ: INOD) Innodata is a global data engineering company delivering the promise of AI to many of the world’s most prestigious companies. We provide AI-enabled software platforms and managed services for AI data collection/annotation, AI digital transformation, and industry-specific business processes. Our low-code Innodata AI technology platform is at the core of our offerings. In every relationship, we honor our 30+ year legacy delivering the highest quality data and outstanding service to our customers.
1001 - 5000 employees
Prompt Engineer – LLM Automation for Data Labeling, Localization
October 7
Innodata Inc.
(NASDAQ: INOD) Innodata is a global data engineering company delivering the promise of AI to many of the world’s most prestigious companies. We provide AI-enabled software platforms and managed services for AI data collection/annotation, AI digital transformation, and industry-specific business processes. Our low-code Innodata AI technology platform is at the core of our offerings. In every relationship, we honor our 30+ year legacy delivering the highest quality data and outstanding service to our customers.
1001 - 5000 employees
📋 Description
• 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.
🎯 Requirements
• 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. • A collaborative mindset with the ability to solve complex technical challenges and work independently as needed. • Exceptional attention to detail and a commitment to delivering high-quality, reliable AI solutions. • Appreciation for issues of Diversity, Equity, and Inclusion in AI.
🏖️ Benefits
• Health insurance • Professional development
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