As a Senior Software Development Engineer in Test with AI, you will help define and execute technology-agnostic quality engineering practices across modern application stacks. You will partner with developers, architects, product owners, and operations teams to improve test automation, release confidence, and engineering productivity while applying AI-assisted SDLC practices responsibly.
What You Have
Key Responsibilities
• Partner with product owners, developers, architects, and cross-functional teams to understand business and technical requirements and translate them into comprehensive test strategies, test cases, automation coverage, and release validation plans.
• Design, develop, and maintain scalable automated test frameworks and test suites across UI, API, service, integration, data, event-driven, end-to-end, regression, performance, and deployment validation layers.
• Drive quality engineering best practices by integrating automated tests into CI/CD pipelines, improving test reliability, reducing manual validation effort, and enabling faster, safer releases.
• Validate distributed, high-volume, and event-driven systems, including message flows, data integrity, service contracts, resilience, observability, operational readiness, and failure recovery scenarios.
• Use AI-assisted engineering tools such as GitHub Copilot or equivalent to improve test design, automation development, test coverage, documentation, regression support, defect analysis, troubleshooting, and engineering productivity while maintaining ownership for correctness and quality.
• Apply prompt engineering, reusable instructions, spec-driven workflows, and agentic concepts to accelerate quality engineering activities in a responsible, reviewable, and measurable manner.
• Create and maintain quality artifacts including test plans, automation strategy, traceability, defect analysis, validation evidence, release readiness summaries, and audit-ready documentation.
• Collaborate with globally distributed teams and influence quality outcomes across the SDLC through automation-first thinking, risk-based testing, engineering rigor, and continuous improvement.
Required Qualifications
• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related technical field, or equivalent practical experience.
• 8+ years of experience in software testing, test automation, software development, or quality engineering for enterprise applications in highly available, high-volume environments.
• Hands-on experience designing, developing, and maintaining automated test frameworks using one or more modern programming languages and test automation ecosystems.
• Strong experience with API testing, UI automation, service validation, integration testing, end-to-end testing, regression automation, test data validation, and release validation practices.
• Experience validating cloud-native, containerized, distributed, or service-based applications deployed on platforms such as PCF, Kubernetes, Docker, Google Cloud Platform, AWS, Azure, or comparable enterprise platforms.
• Experience with SQL or NoSQL databases, including test data creation, data validation, query-based verification, and quality checks across structured or semi-structured data.
• Strong understanding of object-oriented programming, scripting, algorithms, data structures, debugging practices, service contracts, and automation design principles.
• Experience with CI/CD pipeline integration, source control, work tracking, and collaboration tools such as GitHub, GitLab, Azure DevOps, Jira, Rally, Confluence, or comparable platforms.
• Working experience or exposure to messaging, streaming, or event-driven technologies such as Kafka, IBM MQ, RabbitMQ, cloud pub/sub services, or comparable platforms.
• Experience applying BDD, TDD, ATDD, risk-based testing, shift-left testing, quality gates, and modern quality engineering practices across the SDLC.
• Demonstrated hands-on experience using GenAI coding assistants across SDLC workflows, including test generation, automation development, refactoring, unit testing, regression support, code reviews, scripting, troubleshooting, and documentation.
• Practical familiarity with tools such as GitHub Copilot, Claude Code, Gemini Code Assist, Cursor, or equivalent AI-assisted engineering tools in IDE, CLI, or workflow-based environments.
• Working knowledge of Generative AI concepts, responsible AI-assisted engineering practices, prompt engineering, reusable prompts, custom instructions, and AI-augmented SDLC workflows.
• Ability to use AI tools while maintaining strong engineering judgment, code review rigor, validation discipline, data protection awareness, and quality accountability.
• Experience applying automation and AI-driven approaches to improve testing workflows, delivery processes, operational quality outcomes, and modernization initiatives.
• Excellent problem-solving, critical thinking, communication, and decision-making skills, with the ability to work independently in a fast-paced, team-oriented environment.
• Domain / Industry: Understands broker-dealer domain capabilities and operations, with preferred experience in the financial services industry.
• Communication: Communicates clearly and effectively, with proven ability to guide and collaborate with engineers in a team-oriented environment.
• Execution Focus: Brings a collaborative mindset and bias for action, effectively partnering with developers, architects, product owners, and adjacent teams to drive outcomes.
Preferred Qualifications
• Experience as a Software Development Engineer in Test on an Agile Scrum, Kanban, or scaled Agile team.
• Experience with performance, reliability, resiliency, chaos, observability, or production-readiness testing for high-volume distributed platforms.
• Experience in the brokerage, banking, wealth management, capital markets, or broader financial services domain.
• Experience defining automation strategy, framework standards, coding guidelines, quality metrics, and release readiness criteria across multiple teams or platforms.
• Proficiency in creating automation design documentation, functional and technical validation notes, test process documentation, runbooks, and production or test environment documentation.
• Exposure to AI/ML implementation concepts, Large Language Models, agentic workflows, Retrieval-Augmented Generation, or AI-assisted quality engineering practices.
• Experience mentoring engineers or influencing teams on quality engineering, test automation, AI-assisted SDLC adoption, and continuous improvement practices.
Technology-Agnostic Expectations
• This role is not limited to a single programming language or technology stack. The candidate should be capable of applying quality engineering practices across modern enterprise platforms and adapting to the technology used by the team.
• The candidate should demonstrate depth in automation design, test strategy, validation rigor, CI/CD integration, and production-quality engineering rather than relying on one specific tool or framework.
• The candidate should be comfortable learning new tools, frameworks, cloud platforms, automation libraries, and AI-assisted engineering workflows as business and platform needs evolve.
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