ZipRecruiter

Log In

1

Python Automation Testing Jobs in Iowa (NOW HIRING)

Moderna

$20 - $60/hr

Support the development, configuration, and testing of laboratory automation systems, robotic ... Programming experience in Python, C#, JavaScript, MATLAB, or a similar language. * Experience ...

Moderna

$20 - $60/hr

Support the development, configuration, and testing of laboratory automation systems, robotic ... Programming experience in Python, C#, JavaScript, MATLAB, or a similar language. * Experience ...

Moderna

$20 - $60/hr

Support the development, configuration, and testing of laboratory automation systems, robotic ... Programming experience in Python, C#, JavaScript, MATLAB, or a similar language. * Experience ...

previous pagenext page

Showing results 21-40

All JobsPython Automation Testing JobsPython Automation Testing Jobs in Iowa

Python Automation Testing information

See Iowa salary details

$10

$48

$70

How much do python automation testing jobs pay per hour?

As of Sep 13, 2026, the average hourly pay for python automation testing in Iowa is $48.16, according to ZipRecruiter salary data. Most workers in this role earn between $41.54 and $54.86 per hour, depending on experience, location, and employer.

What is Python automation testing?

Python Automation Testing refers to the process of using Python programming language to write scripts that automatically test software applications. These scripts can validate functionality, performance, and reliability of software, reducing the need for manual testing and speeding up the development cycle. Python is popular for automation testing because of its readability, extensive libraries like Selenium and PyTest, and strong community support. Automation tests can be integrated into continuous integration pipelines to ensure consistent quality across software releases.

What are the key skills and qualifications needed for Python automation testing?

To thrive as a Python Automation Testing professional, you need strong proficiency in Python programming, knowledge of software testing methodologies, and experience with test automation frameworks, often supported by a degree in computer science or a related field. Familiarity with tools such as Selenium, PyTest, Jenkins, and version control systems like Git is typically required, along with certifications like ISTQB being advantageous. Analytical thinking, attention to detail, and effective communication skills help testers identify issues, collaborate with teams, and document findings clearly. These competencies ensure the creation of reliable, maintainable automated tests that improve software quality and streamline development cycles.

What are common challenges in Python automation testing, and how can they be addressed?

One common challenge in Python Automation Testing is maintaining test scripts as applications evolve, which can lead to flaky tests or outdated scripts. To address this, it's important to implement modular and reusable code, and regularly review and refactor test cases. Collaborating closely with developers and participating in code reviews can also help testers anticipate changes and adapt their tests proactively. Additionally, integrating robust reporting and logging mechanisms helps quickly identify and resolve issues, ensuring the reliability of the automated test suite.

What is the difference between Python Automation Testing vs Manual Software Testing?

AspectPython Automation TestingManual Software Testing
Required SkillsPython programming, automation tools, scriptingTest case execution, attention to detail, communication
Work EnvironmentAutomated testing frameworks, scripting environmentsTest labs, user environments, manual execution
Industry UsageSoftware development, QA teams, continuous integrationInitial testing phases, exploratory testing, user acceptance

Python Automation Testing involves writing scripts to automate test cases, increasing efficiency and repeatability. Manual Software Testing requires testers to execute test cases manually, focusing on exploratory and usability aspects. Both roles are essential in software quality assurance, but Python Automation Testing emphasizes automation skills, while manual testing emphasizes detailed test execution and observation.

What are popular job titles related to Python Automation Testing jobs in Iowa?

For Python Automation Testing jobs in Iowa, the most frequently searched job titles are:

What job categories do people searching Python Automation Testing jobs in Iowa look for?

The top searched job categories for Python Automation Testing jobs in Iowa are:

What cities in Iowa are hiring for Python Automation Testing jobs?

Cities in Iowa with the most Python Automation Testing job openings:

Python Automation Testing jobs near you
Infographic showing various Python Automation Testing job openings in Iowa as of August 2026, with employment types broken down into 2% As Needed, 83% Full Time, 12% Part Time, 2% Contract, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $100,178 per year, or $48.2 per hour.

AI Developer

West Des Moines, IA • On-site

American technologies consulting
See more jobs at American technologies consultingexternal

Contractor

Re-posted 14 days ago

Key responsibilities

  • Design, build, and deploy autonomous AI agents using frameworks like LangChain and LangGraph.

  • Develop and train machine learning models to enable agent learning, integration, and decision-making.

  • Integrate AI agents with external systems, APIs, and environments, ensuring seamless tool usage and memory management.

Job description

Job Overview

We are seeking a skilled AI Developer to design, build, and deploy autonomous AI agents from scratch. This role involves creating intelligent systems that can perceive environments, make decisions, and execute actions in real-world or simulated scenarios. You will leverage machine learning, Python, and specialized frameworks like LangChain and LangGraph to develop scalable AI agents for applications such as automation, robotics, virtual assistants, or multi-agent simulations.

Key Responsibilities
  • Architect and implement AI agents from the ground up using frameworks such as LangChain for chaining LLMs and tools, and LangGraph for stateful, graph-based agent workflows, including perception modules (e.g., using computer vision or NLP), decision-making logic (e.g., via reinforcement learning or planning algorithms), and action execution components.
  • Develop and train machine learning models using frameworks like TensorFlow, PyTorch, or Scikit-learn to enable agent learning and adaptation, integrating with LangChain/LangGraph for advanced agent orchestration.
  • Integrate AI agents with external systems, APIs, databases, and environments (e.g., simulation tools like OpenAI Gym or real-world interfaces), ensuring seamless tool usage and memory management via LangChain components.
  • Optimize agents for performance, scalability, and robustness, including handling edge cases, ethical considerations, and safety protocols within graph-structured agent designs.
  • Collaborate with cross-functional teams (e.g., data scientists, software engineers) to iterate on agent designs based on feedback and testing.
  • Conduct experiments, simulations, and evaluations to refine agent behaviors and ensure reliability in production.
  • Document code, architectures, and methodologies for reproducibility and team knowledge sharing.
  • Stay current with advancements in AI agent technologies, such as large language models (LLMs), multi-agent systems, and emerging frameworks like LangChain and LangGraph.
Required Skills and Qualifications
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
  • Proficiency in Python programming, with strong experience in ML libraries (e.g., TensorFlow, PyTorch, NumPy, Pandas) and agent-specific tools (e.g., LangChain, LangGraph, AutoGen, RLlib, Hugging Face Transformers).
  • Hands-on experience building AI agents from scratch using LangChain for tool integration and agent chains, LangGraph for multi-step reasoning and state management, including reinforcement learning, state machines, graph-based planning, or evolutionary algorithms.
  • Solid understanding of data structures, algorithms, software engineering principles, and version control (e.g., Git).
  • Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) for deploying agents, and tools like Docker/Kubernetes for containerization.
  • Strong problem-solving skills, with the ability to debug complex systems and work in agile, fast-paced environments.
  • Excellent communication skills to articulate technical designs and collaborate effectively.
Preferred Qualifications
  • Experience with specialized domains like natural language processing (NLP), computer vision, robotics (e.g., ROS), or game AI, integrated with LangChain/LangGraph.
  • Knowledge of big data tools (e.g., Spark, Hadoop) or databases (SQL/NoSQL) for handling large-scale agent data.
  • Prior work with multi-agent systems, ethical AI, or real-time applications using advanced frameworks.
  • Contributions to open-source AI projects or a portfolio demonstrating agent-building expertise (e.g., GitHub repos showcasing LangChain/LangGraph implementations).