Advanced Software Test Tool AI Developer

CFO Trane U.S. -CFO•La Crosse, WI

5d•Hybrid

About The Position

Trane Technologies is seeking an AI Engineer who will help build our next generation of intelligent software testing tools and enhance existing non-intelligent test tools. This role will also mentor others on the software test team in the use of AI for software testing. If you are someone who loves to think of creative ways to apply AI to reduce and augment human effort in software testing this would be the ideal role for you. The Advanced Software Test Tool AI Developer will bring a strong problem-solving mindset with a passion for improving software quality through intelligent automation. This individual should have the ability to balance research-oriented experimentation with pragmatic product delivery and have curiosity about emerging AI technologies and their practical application in engineering tools.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related technical field.
4-6 years of software test and automation experience, with 2–3 years of those including direct AI experience in testing and application development.
Hands-on experience building and deploying ML or AI-enabled applications in production or near-production environments.
Strong programming skills in Python, with experience building backend services, APIs, or automation frameworks.
Experience with test automation tools (Robot/Selenium) and API testing (Postman or similar) and AI integrations.
Experience with software testing concepts such as unit, integration, system, regression, and end-to-end testing.
Model integration with build pipelines, defect management tools (Jenkins, QMetry, Jira).
Understanding of machine learning fundamentals, including model training, evaluation, feature engineering, and experimentation.
Bias toward human-in-the-loop verification — never trusting agent output blindly.
Exposure to LLMs, prompt engineering, vector search, RAG patterns, or generative AI application development.
Ability to analyze complex technical problems and translate them into practical, scalable solutions.
Strong communication and collaboration skills with cross-functional teams.
Experience applying AI to software quality, testing, developer tools, or engineering productivity use cases.
Knowledge of software telemetry, observability data, and log analysis for defect detection or failure prediction.
Understanding of secure software development practices.
Experience working in Agile product development environments.
Exposure to synthetic data generation, code intelligence, static analysis, or test data management solutions.

Responsibilities

Design and develop AI-powered software testing tools to automate and improve software specification analysis, test case creation, automated test script authoring, test execution, defect detection and mitigation.
Build intelligent tooling that helps accelerate QA workflows and integrate supporting tools through automation, machine learning, and generative AI capabilities.
Collaborate with embedded and system software engineers, product managers, and platform teams to create scalable testing products that improve test coverage, release confidence, and engineering productivity.
Design, build, and maintain AI/ML features for advanced software testing platforms: functional specification analysis, test case generation, test script generation, defect prediction, defect analysis, flaky test detection, root cause analysis, and intelligent test prioritization, evaluation criteria for AI components (accuracy, hallucination checks, guardrail adherence), MCP Server integration and development.
Develop and integrate large language model (LLM) and machine learning solutions to improve automated testing for embedded and UI applications.
Create data pipelines and model workflows that ingest application logs, test results, requirements, and code changes to generate actionable testing insights.
Partner with cross-functional teams to identify high-value use cases for AI in test design, regression, optimization, and test maintenance.
Evaluate model performance and useability using appropriate quality metrics and continuously improve accuracy, reliability, latency, and cost efficiency.
Implement prompt engineering, retrieval, fine-tuning strategies, or agent-based workflows where appropriate to support testing use cases.
Contribute to architecture decisions for scalable, secure, and production-ready AI systems.
Ensure solutions follow responsible AI, data privacy, security, and software governance standards.
Document technical designs, experiments, model behavior, and deployment practices to support maintainability and knowledge sharing.