Your Impact Quality Infrastructure is a shared platform team, so your work reaches well beyond your own commits. The frameworks, pipelines, and dashboards you build are used every day by every product scrum team — which means when you build AI-assisted capabilities into them (test scaffolding, failure triage, flaky-test diagnosis, smarter alerting), you change how the entire engineering organization works, not just how one team tests. You will have real latitude to introduce AI-assisted engineering practices here, and a direct hand in setting the standards other engineers follow.
What You'll Do
Use AI-assisted tooling (e.g., Claude Code, GitHub Copilot) as a routine part of development, debugging, and test authoring — and help establish effective, well-judged practices for its use across the engineering organization.
Build AI-assisted capabilities into the quality platforms product teams consume test-generation scaffolding, automated failure categorization (environment vs. code vs. flaky), root-cause summarization, and intelligent alert routing that reduces noise and surfaces signal.
Develop, extend, and maintain automation frameworks used across multiple scrum teams (API-level and UI-level).
Build and maintain CI pipelines (GitHub Actions, Jenkins) that execute end-to-end integration and regression suites, and reduce feedback-loop time through smarter test selection and prioritization.
Troubleshoot CI failures, flaky tests, and environment issues; support product teams in unblocking their workflows.
Contribute to test-reporting and alerting infrastructure: data pipelines, dashboards (Grafana), and integrations (PagerDuty, Slack,Teams).
Collaborate with product scrum teams to understand their testing needs and translate them into framework features, CI improvements, and AI-assisted workflows they will adopt.
Participate in code reviews focusing 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 — so teams can self-serve effectively.
Required Qualifications
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 (e.g., Claude Code, Cursor, GitHub Copilot, or similar) in real development work, with 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 (API, integration, or UI).
Working knowledge of CI/CD concepts and tools (e.g., GitHub Actions, Jenkins, GitLab CI, or similar).
Familiarity with version control workflows in Git (branching, pull requests, 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, and 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 (e.g., pytest, Jest, Postman/Newman, custom frameworks).
Familiarity with Docker/containers and cloud platforms (AWS preferred).
Exposure to any observability or data tooling (Grafana, InfluxDB, ClickHouse, or similar time-series/analytics databases).
Experience with scripting around notifications or integrations (Slack bots, PagerDuty, AWS Lambda).
Basic SQL and comfort working with structured data.
Interest in developer experience, internal tooling, or platform engineering.
About the scope of this role: 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 here is applied: using AI-assisted tooling in your own engineering workflow and building AI-assisted capabilities into the quality platforms our engineering teams use every day.
Why You Will Love It Here!
Flexibility: Hybrid Work Model and Business Casual Dress Code, including jeans
Your Future: Provident Fund
Work/Life Balance: Flexible Personal/Vacation Time Off, Sick Leave, Paid Holidays
Your Wellbeing: Mediclaim, Term Life Insurance, Accidental Coverage, Employee Assistance Program, Parental Leave
Diversity & Inclusion: Committed to Welcoming, Celebrating and Thriving on Diversity
Training: Hands-On, Team-Customized, including SS&C University
Extra Perks: Diwali voucher, Monthly remunerated RnR program
Unless explicitly requested or approached by SS&C Technologies, Inc. or any of its affiliated companies, the company will not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services.
Applications will be accepted on an ongoing basis until the position is filled.
SS&C Technologies is an Equal Employment Opportunity employer and does not discriminate against any applicant for employment or employee on the basis of race, color, religious creed, gender, age, marital status, sexual orientation, national origin, disability, veteran status or any other classification protected by applicable discrimination laws.
