Automation Testing-AI

Role & responsibilities

Job Description: AI QA / LLM Automation Engineer

Job Overview

We are seeking a highly skilled and innovative AI QA / LLM Automation Engineer to join our team. In this pivotal role, you will be responsible for designing, developing, and executing sophisticated testing strategies tailored specifically for generative AI systems. You will play a critical role in evaluating Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, and autonomous AI agents to ensure high accuracy, contextual relevance, and robust logical reasoning.

Key Roles & Responsibilities

• AI Model Validation: Design and execute comprehensive test strategies specifically for evaluating LLM responses. Focus heavily on measuring accuracy, ensuring long-term context retention, and drastically reducing or mitigating hallucinations.
• AI Agent Testing: Rigorously test the underlying logic, complex state management, and tool-calling capabilities of autonomous AI agents across various edge cases.
• Python Automation: Develop, maintain, and scale custom automated testing frameworks from scratch using Python.
• CI/CD Integration: Continuously evaluate AI models by seamlessly integrating custom testing and evaluation frameworks into existing CI/CD pipelines.
• Evaluation Metrics: Define and implement novel evaluation metrics for non-deterministic AI features where standard deterministic software testing (exact-match) falls short.
• Cross-Functional Collaboration: Work closely with AI developers, data scientists, and product managers to establish quality benchmarks for AI-driven features.

Required Skills & Qualifications

• Programming: Strong programming proficiency in Python, with an emphasis on writing clean, scalable, and maintainable automation scripts.
• AI/ML Knowledge: Deep understanding of Large Language Model (LLM) architectures, prompt engineering, and model behaviors.
• Framework Experience: Hands-on experience building or testing RAG (Retrieval-Augmented Generation) pipelines and agentic frameworks (e.g., LangChain, LlamaIndex).
• Non-Deterministic Testing: Proven experience in validating non-deterministic outputs where traditional exact-match assertions do not apply (e.g., semantic similarity, LLM-as-a-judge, or custom evaluation heuristics).
• Automation & DevOps: Solid understanding of CI/CD tools (e.g., GitHub Actions, GitLab CI, Jenkins) and version control (Git).
• Problem Solving: Strong analytical mindset with the ability to foresee edge cases in generative AI applications.
• Key Responsibilities:
• AI Model Validation: Design and execute test strategies specifically for evaluating LLM responses, focusing on accuracy, context retention, and reducing hallucinations.
• AI Agent Testing: Test the logic, state management, and tool-calling capabilities of autonomous AI agents.
• Python Automation: Develop custom automated testing frameworks using Python to continuously evaluate AI models in CI/CD pipelines.
• Required Skills:
• Strong programming proficiency in Python.
• Deep understanding of LLM architectures, RAG (Retrieval-Augmented Generation), and agentic frameworks (e.g., LangChain, LlamaIndex).
• Experience validating non-deterministic outputs where traditional exact-match assertions do not apply.