SSE Test Engineering AI

Company Overview

Arcesium is a global financial technology firm that solves complex data-driven challenges faced by some of the world’s most sophisticated financial institutions. We constantly innovate our platform and capabilities to meet tomorrow’s challenges, anticipate the risks our clients encounter, and design advanced solutions to help our clients achieve transformational business outcomes.

Financial technology is a high-growth industry as change and innovation continue to disrupt the status-quo and prompt major transformation. Arcesium is at a particularly interesting time in our own growth as we look to leverage our successfully established market position and expand operations in pursuit of strategic new business opportunities. We value intellectual curiosity, proactive ownership, and collaboration with colleagues, and we empower you to meaningfully contribute from day one and accelerate your professional development.

Team Summary

Test Engineering team plays a pivotal role in driving software quality with Arcesiums mission to power the entire investment lifecycle, helping clients achieve transformational business outcomes through its consultative approach, cloud-native technology, and deep domain expertise by ensuring correctness, completeness, security, and reliability, the team safeguards product excellence—leveraging cutting-edge technologies and AI-driven approaches to elevate quality standards.

What You’ll Do

• Design, develop, and maintain automated test frameworks, tools, and scripts to validate complex financial applications and services.
• Collaborate with developers, product managers, and QA teams to ensure high-quality deliverables from planning through deployment.
• Analyze test results, report defects, and ensure clarity and reproducibility of issues.
  
  

• A Bachelors degree in Computer Science or a related discipline.
• Solid fundamentals in OOP concepts, data structures, databases and algorithms applied to test automation.
• Strong understanding of SDLC, QA life cycle and various testing methodologies including API, frontend and backend testing
• Expertise with Linux/Unix systems, Jenkins, GIT, or equivalent industry-standard tools.
• Experience with test management tools such as JIRA, TestRail, qTest, QC or similar tools.
• Strong debugging, analytical, and problem-solving skills.
• Familiarity with AI-driven testing tools (e.g., Testim, Mabl, Functionize, Diffblue, or LLM-based test generation).
  
  

• Performance and load testing experience (JMeter, Locust, Gatling).
• Exposure to containerization and orchestration (Docker, Kubernetes).
• Experience with cloud platforms (AWS, GCP, Azure) and testing cloud-native applications.
• Hands-on experience with at least one AI/ML library or platform (e.g., Scikit-learn, Hugging Face, TensorFlow, or LangChain) for tasks like:
• Generation or optimizimation of test cases from requirements/user stories.
• Identify anomalies, flaky tests, and defect-prone modules.
• Automate test data generation and validation.
• Exposure to Generative AI (GenAI) technologies and their application in software testing.