QA Engineer

Overview We are seeking a highly analytical Quality Assurance Engineer to validate, evaluate, and improve complex electromechanical automation systems used in rail technology environments. These systems integrate electrical, mechanical, software, networking, controls, sensors, and AI-enabled components. This is a systems-level quality engineering role focused on understanding how complete systems perform in real-world conditions, identifying where performance differs from design intent, determining root causes, and driving measurable improvements in quality, reliability, safety, efficiency, and scalability. The role combines verification and validation, hands-on troubleshooting, defect investigation, operational data analysis, root cause analysis, and continuous process improvement across engineering and field operations. Key Responsibilities Develop and execute test plans, test cases, validation procedures, and acceptance criteria for integrated electromechanical automation systems. Perform functional, integration, regression, system, and failure-condition testing across hardware, software, sensors, actuators, controls, and AI-enabled functions. Identify, reproduce, document, and track defects through resolution and confirm corrective actions address the underlying root cause. Troubleshoot electrical and electromechanical issues using schematics, measurements, system data, and structured diagnostic methods. Analyze defect, failure, performance, and operational data to identify trends, recurring issues, and improvement opportunities. Apply structured problem-solving methods including root cause analysis, 5 Whys, Pareto analysis, FMEA, DMAIC, and corrective/preventive action. Evaluate manual and automated processes for opportunities to improve safety, quality, throughput, consistency, reliability, and cost. Quantify improvement and automation opportunities using metrics such as cycle time, labor utilization, defect reduction, and throughput. Evaluate interactions between electrical, mechanical, software, controls, sensors, and physical process variables. Collaborate with electrical, mechanical, software, AI, DevSecOps, quality, and operations teams to resolve complex system issues. Create clear technical documentation, including test results, defect reports, investigation summaries, quality records, and improvement recommendations. Required Qualifications Bachelor s degree in Engineering, Quality Engineering, Industrial Engineering, Manufacturing Engineering, Systems Engineering, Computer Science, or a related technical field, or equivalent relevant experience. Experience in quality engineering, systems testing, process engineering, manufacturing engineering, industrial automation, systems engineering, or a closely related field. Strong understanding of electrical and electromechanical systems, including sensors, actuators, digital and analog signals, I/O, relays, motors, power systems, and basic controls. Ability to read and interpret electrical schematics and troubleshoot electrical and electromechanical systems. Experience developing and executing test plans, documenting defects, analyzing results, and validating corrective actions. Demonstrated experience with root cause analysis, structured problem-solving, and data-driven decision-making. Ability to understand interactions across electrical, mechanical, software, controls, and physical systems. Strong analytical skills with the ability to identify trends, distinguish symptoms from root causes, and prioritize issues based on impact. Ability to evaluate physical work processes and identify opportunities for automation, efficiency, quality, safety, and reliability improvements. Strong written and verbal communication skills with the ability to communicate technical findings to engineering and non-technical stakeholders. Preferred Qualifications Experience with Lean, Six Sigma, DMAIC, FMEA, SPC, process capability, CAPA, or related continuous-improvement methods. Experience with industrial automation, robotics, manufacturing systems, machine controls, machine vision, PLCs, or other complex physical systems. Ability to read and understand basic C++ code or software logic; software development expertise is not required. Experience with time studies, process mapping, task analysis, work measurement, or automation opportunity assessment. Experience using data analysis tools such as Excel, SQL, Python, MATLAB, or similar technologies. Experience with automated testing, scripting, test frameworks, version control, CI/CD, DevSecOps, or modern software-development practices. Exposure to AI/ML systems and testing approaches for AI-driven or adaptive system behavior. Candidate Profile The ideal candidate is a hands-on, systems-oriented engineer who can determine how a machine or automated system should perform, identify what actually occurred, isolate the source of a failure, and help prevent recurrence. Strong candidates may come from quality engineering, manufacturing engineering, process engineering, systems engineering, automation, robotics, or electromechanical troubleshooting backgrounds. Software engineering expertise is not required, but candidates should be comfortable working with software engineers and understanding how software and control logic affect physical system behavior.