Job Information
💼 Experience
3+ Years
3+ Years
🤖 Domain
AI Quality Assurance
AI Quality Assurance
📝 Focus
Prompt Engineering
Prompt Engineering
⚙️ Technology
LLMs & AI Systems
LLMs & AI Systems
Core Skills
Prompt Engineering
LLM Testing
AI QA
Model Evaluation
Prompt Optimization
AI Quality Metrics
Python
MLflow
LangChain
RAG
Vector Databases
AI Testing Areas
- Prompt Engineering Validation
- LLM Response Evaluation
- AI Bias Detection
- Failure Mode Analysis
- Prompt Optimization Testing
- AI Quality Framework Design
- Automated AI Testing
- RAG System Validation
Key Responsibilities
- Design and execute test plans for AI and LLM-based applications.
- Create and optimize prompts for various AI use cases.
- Identify model limitations, biases, and failure patterns.
- Develop AI testing frameworks and evaluation processes.
- Collaborate with AI engineers to improve model quality.
- Measure and track AI performance using evaluation metrics.
Required Skills
- 3+ years of QA experience with AI/ML testing exposure.
- Strong understanding of LLMs and prompt engineering.
- Experience with Python-based test automation.
- Knowledge of AI evaluation methodologies and metrics.
- Strong analytical and problem-solving abilities.
- Excellent communication and documentation skills.
Preferred Skills
- Experience with MLflow, LangChain, or similar frameworks.
- Knowledge of Retrieval-Augmented Generation (RAG).
- Understanding of vector databases and AI architectures.
- Background in NLP, Data Science, or Machine Learning.
Why This Role Matters
Prompt Engineering and AI Quality Assurance are becoming essential for enterprise AI adoption. This role helps improve the reliability, performance, and safety of AI systems by combining prompt optimization, model evaluation, and structured quality testing practices.
Disclaimer: This job information is shared for career guidance purposes only. Applications are processed through the employer's official recruitment platform.