Software Development Engineer in Test (SDET) — AI-Enabled Quality Infrastructure

Overview

Join the Quality Infrastructure team as a Software Development Engineer in Test (SDET) building AI-assisted automation platforms, frameworks, CI pipelines, and reporting systems for the SS&C Advent Eze investment platform. This software engineering role focuses on quality infrastructure and applied AI-assisted engineering practices rather than machine learning, data science, or writing product tests directly.

What you'll do

• Use AI-assisted tooling, such as Claude Code and GitHub Copilot, in development, debugging, and test authoring, and help establish effective practices for its use across the engineering organization.
• Build AI-assisted capabilities into quality platforms, including test-generation scaffolding, automated failure categorization, root-cause summarization, and intelligent alert routing.
• Develop, extend, and maintain API-level and UI-level automation frameworks used across multiple scrum teams.
• Build and maintain GitHub Actions and Jenkins CI pipelines for end-to-end integration and regression suites.
• Reduce feedback-loop time through smarter test selection and prioritization.
• Troubleshoot CI failures, flaky tests, and environment issues, and support product teams in unblocking their workflows.
• Contribute to test-reporting and alerting infrastructure, including data pipelines, Grafana dashboards, and PagerDuty, Slack, and Teams integrations.
• Collaborate with product scrum teams to understand testing needs and translate them into framework features, CI improvements, and AI-assisted workflows.
• Participate in code reviews focused on reliability, maintainability, and developer experience, including reviewing AI-generated contributions with appropriate scrutiny.
• Document framework usage, onboarding guides, and runbooks, including guidance on where AI-assisted workflows help and where they do not.

What you'll need

• 2-4 years of professional experience in software engineering, test automation, or a related SDET/QA engineering role.
• Hands-on, practical use of AI coding assistants, such as Claude Code, Cursor, GitHub Copilot, or similar, in real development work.
• Demonstrated judgment about when to accept, verify, rewrite, or reject AI-generated output.
• Solid programming skills in at least one language: JavaScript/TypeScript, Python, or similar.
• Hands-on experience building or maintaining automated test suites at the API, integration, or UI level.
• Working knowledge of CI/CD concepts and tools, such as GitHub Actions, Jenkins, GitLab CI, or similar.
• Familiarity with version control workflows in Git, including branching, pull requests, and code review.
• Prior experience on a product development scrum team or in an Agile environment.
• Understanding of the development lifecycle from feature work through deployment.
• Strong problem-solving skills.
• Clear written and verbal communication.
• A service-oriented mindset toward internal customers.
• Willingness to advocate for and teach AI-assisted engineering practices to peers and partner teams.

Nice to have

• Experience integrating LLM APIs or AI-assisted automation into developer tooling, CI pipelines, or internal services.
• Exposure to agentic development tooling, prompt design, or evaluating AI output quality in an engineering workflow.
• Experience with Cypress, Playwright, or similar browser-automation frameworks.
• Exposure to REST API testing tools and frameworks, such as pytest, Jest, Postman/Newman, or custom frameworks.
• Familiarity with Docker/containers and cloud platforms, with AWS preferred.
• Exposure to observability or data tooling, such as Grafana, InfluxDB, ClickHouse, or similar time-series/analytics databases.
• Experience with scripting around notifications or integrations, such as Slack bots, PagerDuty, or AWS Lambda.
• Basic SQL and comfort working with structured data.
• Interest in developer experience, internal tooling, or platform engineering.

Details

• Location: Hyderabad, India.
• Hybrid work model.
• This is a software engineering role, not a machine learning or data science role.
• No model-training or data science background is required.
• The AI work is applied: using AI-assisted tooling in your engineering workflow and building AI-assisted capabilities into quality platforms.