Prompt Engineer (LLM Automation for Data Labeling & Localization)
Share
Job Location:
Ridgefield Park, NJ - USA
Monthly Salary:
Not Disclosed
Posted on:
06-11-2025
Vacancies:
1 Vacancy
Job Summary
Job description
About the Role
Innodata is building a team of prompt engineers to harness the power of LLMs to automate data annotation and human evaluation workflows. The goal is to facilitate accurate localized and culturally adapted data labeling and translation processes through effective prompt design and implementation. This team will collaborate directly with our client partner a leading technology company to identify opportunities for automation design solutions and drive measurable improvements. As a technical subject matter expert you will work backwards from the customer problem statement to develop an efficient plan for execution.
You will collaborate with cross-functional teams including product managers data scientists and client teams to solve complex problems reduce human effort and ensure that AI-driven processes meet high standards for quality and reliability. Your work will directly contribute to improving our clients data annotation and evaluation processes enabling them to scale more efficiently.
Key Responsibilities
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.
Job requirements
Technical & Required Skills
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.
Preferred Skills and Experience
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.
Knowledge of another language.
Minimum Education Requirements
Bachelors degree or higher in Computer Science Artificial Intelligence Machine Learning Linguistics Localization or a related field.
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment banking details or sensitive personal information during the application process. To learn more on how to recognize job scams please visit the Federal Trade Commissions guide at you believe youve been targeted by a recruitment scam please report it to Innodata at and consider reporting it to the FTC at .
#LI-NS1
All done!
Your application has been successfully submitted!
Key Skills
- Continuous Integration
- APIs
- Automotive software
- Test Cases
- Electrical Engineering
- Junit
- Distributed Control Systems
- Testng
- Java
- Test Automation
- Programmable Logic Controllers
- Selenium
