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Prompt Engineer (LLM Automation – Data Labeling & Localization)
Canada
Not specified
March 25, 2026
Job description
About the Role
Innodata is hiring Prompt Engineers to design and automate data labeling and localization workflows using large language models (LLMs).
You’ll work directly with data scientists, linguists, and product teams to build scalable AI-driven systems that reduce manual annotation effort while maintaining high accuracy and cultural relevance.
What You’ll Do
- Design and implement prompts for:
- Data labeling
- Localization
- Translation workflows
- Prototype and validate LLM-based solutions
- Analyze model performance using KPIs and evaluation metrics
- Optimize prompts through user testing and feedback
- Build and improve AI-driven data pipelines
- Collaborate with cross-functional teams (ML, product, linguistics)
- Develop guidelines and documentation for prompt usage
- Improve automation in annotation and evaluation workflows
Requirements
Core Skills
- Strong understanding of LLMs (transformers, evaluation, bias, reliability)
- Experience using LLMs programmatically
- Python expertise (data processing, NLU, automation)
- Familiarity with:
- APIs (OpenAI, Hugging Face)
- Data formats (JSON, XML, JavaScript)
- Experience with ML frameworks (TensorFlow, PyTorch)
- Understanding of data pipelines and automation systems
Additional Requirements
- Knowledge of localization and cultural nuances
- Strong analytical and problem-solving skills
- Ability to communicate technical insights clearly
- High attention to detail
Preferred
- 2+ years in prompt engineering or AI/ML roles
- Experience with annotation tools (e.g., Labelbox)
- Familiarity with cloud platforms and deployment
- Multilingual knowledge
Education
- Bachelor’s degree or higher in:
- Computer Science
- AI / Machine Learning
- Linguistics or related field
Why This Role Matters
- Work on real-world LLM automation systems
- Reduce human effort in large-scale AI pipelines
- Contribute to high-impact AI infrastructure used globally
Apply now
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