Prompt & Python Developer - Welocalize

As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over 250 languages with a growing network of over 400,000 in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them. www.welocalize.com

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

This role bridges prompt engineering, Python development, and data-driven testing. The ideal candidate is fluent in Python’s standard libraries, experienced with test automation, and comfortable working with datasets to validate and refine outputs.

. Design and refine effective prompts to achieve specific outcomes in language models.

. Translate business or research requirements into prompt structures and automated workflows.

. Implement prompt logic directly in Python applications

. Write clean, maintainable Python code using standard libraries (e.g., os, json, re, itertools, collections, multiprocessing).

. Build modular scripts and frameworks for data processing, evaluation, and automation.

. Validate outputs against benchmarks, datasets, and acceptance criteria.

. Debug and optimize to reduce errors, inconsistencies, or unexpected results.

. Design, clean, and preprocess datasets for training, testing, and validation

. Maintain dataset versioning and documentation for reproducibility.

. Implement scripts to generate synthetic test data where necessary.

. Work closely with data scientists, ML engineers, and product stakeholders.

. Contribute to documentation, best practices, and internal knowledge sharing.

. Participate in code reviews and collaborative design discussions.