Data Annotator AI

AI Engineer / Junior AI Engineer with strong programming fundamentals, good problem-solving ability, and a clear understanding of Data Structures, Algorithms, Machine Learning, and modern AI concepts.

The candidate should be able to write clean code, understand basic ML workflows, work with data, and contribute to AI-based automation initiatives such as test case generation, test script generation, RAG-based document understanding, data generation, and intelligent comparison utilities.

The candidate is expected to have coding ability, logical thinking, and basic AI/ML understanding.

Key Responsibilities: • Develop and support AI-based internal tools and automation accelerators. • Write clean and maintainable code using Python, Java, or JavaScript. • Apply Data Structures and Algorithms to solve logical and coding problems. • Work on AI-assisted test case generation, test script generation, and automation productivity tools. • Support RAG-based solutions for requirement understanding, document processing, and test asset generation. • Assist in building utilities for test data generation, file comparison, data validation, and automation support. • Understand and apply basic Machine Learning and NLP concepts in practical use cases. • Work with LLMs, prompts, embeddings, vector databases, and retrieval-based workflows. • Collaborate with QA automation architects, developers, and business teams to convert requirements into AI-enabled solutions. • Debug issues, analyze outputs, improve accuracy, and optimize generated results. • Learn and adapt to new AI frameworks, models, APIs, and automation tools.

Required Skills: Programming and Problem Solving; AI / ML Fundamentals; NLP, LLM and Transformer Awareness.

Tools and Frameworks: • Python libraries such as NumPy, Pandas, Scikit-learn, or similar. • Basic exposure to AI/LLM frameworks such as LangChain, LlamaIndex, Hugging Face, OpenAI-compatible APIs, or similar tools.

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