What are the responsibilities and job description for the AI Quality Engineering Engineer position at Princeton IT Services?
Job Details
Job Title: AI QE Engineer
Location: Canada
Role Summary
We are looking for an AI Quality Engineering Engineer with strong automation expertise and hands-on experience validating LLMs, GenAI workflows, and AI-driven applications. This role involves building automated test suites, validating AI outputs, ensuring system reliability, and contributing to the quality strategy for next-generation AI features.
Core Experience
- 4 6 years as a Software Engineer, SDET, or Automation Engineer.
- Strong coding skills in Python, TypeScript, or Java.
- Hands-on experience developing automation scripts, tools, or frameworks.
- Practical experience using LLMs, prompt engineering, and evaluating AI-generated outputs.
- Familiarity with agentic AI systems and exposure to tools like LangGraph, AutoGen, CrewAI.
- Basic understanding of Model Context Protocol (MCP) or context-aware workflow automation (nice to have).
AI / ML Technologies
- Practical experience with AI/ML frameworks: LangChain, Hugging Face, GPT models, vector databases, RAG pipelines.
- Experience with ML/DL libraries such as:
Scikit-learn, PyTorch, TensorFlow, Keras, Transformers, OpenCV. - Ability to work with embeddings, similarity search, and content evaluation metrics.
GenAI & AI Agent Development
- Ability to integrate or build GenAI components, including RAG pipelines or agent-based workflows.
- Support model evaluation tasks such as:
- Output quality checks
- Hallucination detection
- Prompt validation
- Regression checks for model updates
Automation & Quality Engineering
- Experience building automation using Python, PyTest, Selenium, Playwright, or API testing libraries.
- Ability to design and execute automated tests for:
- Functional
- Integration
- API
- Basic performance & reliability testing
- Hands-on experience testing RESTful APIs and building automated API suites.
Cloud, DevOps & CI/CD
- Exposure to deploying AI or automation solutions on AWS.
- Working knowledge of CI/CD pipelines, including:
- Automated testing
- Model validation steps
- Versioning & artifact management
SDLC & Collaboration
- Strong understanding of the software development lifecycle, including requirements, development, testing, and defect analysis.
- Ability to collaborate with Developers, Data Scientists, and QE teams, clearly communicating progress, risks, and results.
- Skilled in documenting and tracking defects and participating in defect triage.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.