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Temporary Artificial Intelligence Testing information

What is temporary artificial intelligence testing?

A Temporary Artificial Intelligence Testing job involves evaluating and verifying the performance, accuracy, and reliability of AI systems or software for a fixed period, often as part of a project or during peak development cycles. Testers in this role identify bugs, suggest improvements, and ensure that the AI behaves as expected under different scenarios. These positions are usually contract-based or short-term, making them ideal for people looking to gain experience in the AI field or contribute to specific projects. Responsibilities may include writing test cases, analyzing test results, and collaborating with developers and data scientists to enhance system performance.

What does temporary artificial intelligence testing involve?

As a Temporary Artificial Intelligence Testing professional, you'll typically be assigned to evaluate AI models or software for accuracy, fairness, and reliability. Your tasks may include creating and executing test cases, reporting bugs, and providing feedback on AI system outputs. You'll often collaborate with data scientists, developers, and QA teams to ensure the AI solutions meet performance standards. The work is dynamic and can involve adapting quickly to new testing protocols or shifting project priorities.

What are the key skills and qualifications needed for temporary artificial intelligence testing?

To thrive as a Temporary Artificial Intelligence Tester, you need a solid understanding of software testing principles, basic programming or scripting skills, and familiarity with AI concepts, supported by relevant education or experience. Proficiency with test management tools, bug tracking systems, and AI testing platforms (such as TensorFlow or PyTorch) is typically required. Attention to detail, analytical thinking, and effective communication are essential soft skills for identifying issues and collaborating with development teams. These skills ensure accurate testing, reliable AI model performance, and effective problem-solving in fast-paced project environments.

What is the difference between Temporary Artificial Intelligence Testing vs Data Analyst?

AspectTemporary Artificial Intelligence TestingData Analyst
CredentialsTypically requires knowledge of AI, machine learning, and programming languagesRequires degrees in statistics, mathematics, or related fields; often includes certifications in data analysis tools
Work EnvironmentOften project-based, involving AI model evaluation in tech or research settingsUsually office-based, analyzing data sets in corporate or research environments
Employer & Industry UsageUsed by tech companies, AI startups, and research institutions for testing AI modelsEmployed across industries like finance, healthcare, marketing for data-driven decision making

Temporary Artificial Intelligence Testing focuses on evaluating AI models and algorithms, often requiring programming and machine learning skills. Data Analysts interpret data to inform business decisions, requiring statistical expertise. While both roles involve data and analysis, AI Testing is more specialized in AI model validation, whereas Data Analysts work broadly with data interpretation across industries.

What are the most commonly searched types of Artificial Intelligence Testing jobs in Illinois?

The most popular types of Artificial Intelligence Testing jobs in Illinois are:

What cities in Illinois are hiring for Temporary Artificial Intelligence Testing jobs?

Cities in Illinois with the most Temporary Artificial Intelligence Testing job openings:

Temporary Artificial Intelligence Testing jobs near you

Artificial Intelligence Architect

Appvion, LLC

Highland Park, IL

Full-time

Posted 27 days ago

Appvion rating

8.3

Company rating: 8.3 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

Job description

About the Role

We're hiring an AI Architect to define the technical foundation for all our AI/ML systems including architecture standards, platform decisions, and quality gates that let us deliver scalable, secure, and governed AI solutions tied directly to business outcomes. You'll sit at the intersection of engineering, data, and business strategy, designing the systems and setting the standards that accelerate AI adoption across the enterprise.

What You'll Do

  • Design the enterprise AI/ML architecture, including reference patterns and multi-entity / multi-tenant architectures with governed data boundaries
  • Evaluate and select AI platforms, frameworks, and cloud services
  • Establish technical standards for model development, testing, and deployment
  • Design agentic search and retrieval systems for enterprise knowledge grounding
  • Review and approve architecture for all AI use cases after they reach production
  • Define data architecture requirements for ML pipelines
  • Lead build vs. buy evaluations for AI tooling
  • Mentor technical team members and drive engineering excellence
  • Stay current on AI/ML technology trends and assess their relevance to our roadmap

Qualifications

  • 8+ years in software or data architecture, with 4+ years focused on ML systems
  • Deep expertise in cloud platforms (AWS, Azure, or GCP) and their ML services
  • Proven experience designing production ML pipelines at enterprise scale
  • Strong understanding of MLOps, model monitoring, and deployment patterns
  • Experience with both traditional ML and modern LLM/GenAI architectures
  • Familiarity with core enterprise infrastructure architecture

Skills

  • Languages: Python, SQL, and Scala for ML and data engineering
  • ML frameworks: PyTorch, TensorFlow, scikit-learn, and Hugging Face
  • MLOps: Docker, Kubernetes, CI/CD, MLflow, and model registries
  • Cloud & data: AWS, Azure, GCP, Spark, Airflow, and feature stores
  • LLM, GenAI & agentic search: RAG, fine-tuning, prompt engineering, vector databases, query planning, tool use, retrieval orchestration, and multi-step reasoning
  • Responsible AI: governance, model monitoring, and security by design
  • Solution mindset: design thinking, trade-off analysis, and pragmatic delivery

*LI-MD1

What Appvion employees say

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