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.
Requirements
Strong proficiency in Python (3.x) with deep knowledge of standard libraries.
Experience in prompt engineering and working with LLM-driven workflows.
Hands-on experience with dataset preparation, cleaning, and validation.
Familiarity with version control (Git/GitHub) and collaborative workflows.
Familiarity with project management tools (Jira)
Strong problem-solving, debugging, and analytical skills.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
Pythonprompt engineeringdata processingdataset preparationdataset cleaningdataset validationdebuggingautomationsynthetic test data generationversion control