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MindBridge Analytics Inc.
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We are looking for a QA & Test Automation Developer to help us evolve how software quality is built, automated, and scaled across MindBridge.
Strong testing fundamentals are essential to this role. We are looking for someone who understands common testing heuristics, can identify meaningful product risks, and knows how to test and verify software beyond the obvious happy paths. You will use agentic tools to apply those skills faster and at greater scale without compromising testing depth or judgment.
You will design and maintain automated test suite, improve our quality engineering frameworks and delivery pipelines, and expand the use of AI agents within our QA processes. This includes creating, configuring and using agentic tools to accelerate test creation, analysis, failure triage, coverage assessment, and other recurring quality workflows.
As our product and software development practices become increasingly AI-native, you will also contribute to how we validate AI-powered features. Having direct experience testing AI agents, LLM-based features, or other non-deterministic systems is a strong asset.
Apply strong testing fundamentals and heuristics to identify product risks, edge cases, failure modes, and gaps in expected behaviour Investigate failures and unexpected behaviour and act of them. Configure and use agentic tools with various degree of autonomy, to accelerate test creation, maintenance, analysis, failure triage, and other recurring QA activities. Advocate for testability and quality throughout the development lifecycle. Improve the reliability, performance, and scalability of our test frameworks, internal tooling, and automated quality gates. Evaluate AI-generated outputs critically and ensure appropriate validation and human oversight remain part of the testing process.
Contribute to test strategies for AI-powered, LLM-based, and agentic product capabilities. Help define how non-deterministic behaviour should be evaluated, including expected behaviour, quality thresholds, regression approaches, and failure analysis. Validate areas such as model responses, tool usage, grounding, consistency, error handling, latency, and end-to-end agent behaviour where applicable.
Collaborate with Engineering, Product, and other technical teams to make quality an integral part of software development. Share testing patterns, tools, and lessons learned, and continuously look for ways to improve testing effectiveness and delivery speed.
Strong software testing fundamentals, such as critical thinking, exploratory testing, test design, risk-based testing, common testing heuristics, and the ability to reason about customer and product impact. Hands-on experience designing and developing automated tests for modern software applications. Strong programming skills in at least one language commonly used for test automation or engineering, such as TypeScript/JavaScript or Python. Experience with modern test automation frameworks Experience working with CI/CD pipelines and incorporating automated testing into software delivery workflows. Ability to troubleshoot complex software and automated test failures across application, test, data, and infrastructure layers. Practical experience leveraging AI-assisted development capabilities to automate or improve engineering and QA workflows. Strong collaboration and communication skills, with the ability to work effectively across QA, Engineering, Product, and other technical teams. A continuous-improvement mindset and an interest in finding better ways to make software quality faster, more scalable, and more reliable.
Experience testing AI-powered applications, LLM-based features, copilots, or autonomous or agentic systems. Experience developing evaluation frameworks or automated evaluations for non-deterministic AI behaviour. Experience building automation using agentic technologies that perform multi-step tasks or interact with engineering systems and tools. Familiarity with concepts such as prompt evaluation, tool calling, grounding, retrieval-augmented generation, model evaluation, and LLM observability. Experience with large-scale end-to-end test suites, parallel test execution, test data management, and distributed CI environments.
Fulfill requirements necessary to obtain full background check.
The expected base salary range for this position is to $108,000 to $121,000 and may be eligible for bonus awards. The determination of an applicant’s base salary within this range is based on the individual’s location, skills, experience and competencies, and unique qualifications
Originally posted on Himalayas
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