Job Description:
Job Title: Senior QA Engineer / AI-Driven Test Automation Lead, Counterparty Dashboard, Security Service, CPA/CDA
Corporate Title: Vice President
Location: Pune, India
Role Description
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We are hiring a leader who can transform testing using AI across business-critical platforms. In this role, you will define and drive the test automation strategy across batch systems, services, and user interfaces , ensuring quality is engineered into every layer of the stack. You will enable teams to use AI tools to generate test cases, improve coverage, and detect issues earlier in the development cycle.
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You will set standards for automation, ensure high-quality releases, and drive a shift towards faster, smarter, and more reliable testing practices. You will champion the adoption of AI in quality engineering — from intelligent test generation and self-healing automation to anomaly detection and predictive defect analysis.
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This is a leadership role for someone who can lead large-scale improvements in quality through AI-driven practices , working closely with engineering, product, and business teams across the Counterparty Dashboard, Security Service, and CPA/CDA platforms.
What we’ll offer you
As part of our flexible scheme, here are just some of the benefits that you’ll enjoy,
- Best in class leave policy.
- Gender neutral parental leaves
- 100% reimbursement under childcare assistance benefit (gender neutral)
- Sponsorship for Industry relevant certifications and education
- Employee Assistance Program for you and your family members
- Comprehensive Hospitalization Insurance for you and your dependents
- Accident and Term life Insurance
- Complementary Health screening for 35 yrs. and above
Your key responsibilities
- Define Test Automation Strategy: Own and drive the end-to-end test automation strategy spanning batch systems, microservices/APIs, and UI layers.
- AI-Driven Quality Engineering: Introduce and scale the use of AI tools and frameworks for test case generation, test data synthesis, coverage analysis, and intelligent defect detection.
- Shift-Left Testing: Embed quality early in the SDLC by enabling developers and QA engineers to detect issues during design, coding, and pre-merge stages.
- Automation Standards & Frameworks: Establish standards, reusable frameworks, and best practices for automation across functional, regression, performance, and resilience testing.
- Release Quality Ownership: Drive high-quality, predictable releases through robust CI/CD-integrated test pipelines, quality gates, and automated regression suites.
- Coverage & Risk-Based Testing: Use AI-assisted analytics to identify coverage gaps, prioritize tests based on risk, and optimize test execution time.
- Batch & Data Validation: Architect automated validation strategies for batch jobs, data pipelines, and reconciliations typical of counterparty, security service, and CPA/CDA workflows.
- Tooling & Platform Leadership: Evaluate, select, and roll out modern testing tools (AI-powered and traditional) across the engineering organization.
- Mentorship & Enablement: Mentor QA engineers and SDETs on AI-driven testing practices, automation design, and quality engineering principles.
- Stakeholder Collaboration: Partner with engineering leads, product owners, and business stakeholders to align quality goals with delivery timelines and risk posture.
- Metrics & Continuous Improvement: Define and track quality KPIs (defect leakage, automation coverage, MTTR, escape rate) and continuously improve testing maturity.
Your skills and experience
Core Test Automation
- 9–12 years of experience in software quality engineering, with strong hands-on expertise in test automation across API, UI, and batch systems.
- Deep experience with automation frameworks such as Selenium, Playwright, Cypress, RestAssured, Karate, Cucumber, TestNG, JUnit , or equivalents.
- Strong experience automating backend services, microservices, and event-driven systems (Kafka, MQ, etc.).
- Proven track record of building and scaling batch and data validation test suites (ETL, reconciliations, scheduled jobs).
- Solid programming skills in Java (and ideally one of Python / TypeScript / Groovy).
- Experience integrating automated tests into CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI, Azure DevOps).
AI in Testing
- Hands-on experience using AI/LLM-based tools in testing — for test case generation, test data creation, exploratory testing, and intelligent regression selection.
- Familiarity with AI-powered testing platforms (e.g., Testim, Mabl, Functionize, Applitools, Diffblue, GitHub Copilot for tests, or equivalents).
- Understanding of how to apply LLMs, embeddings, and prompt engineering to QA workflows (requirement-to-test mapping, log/defect triage, test summarization).
- Experience with self-healing automation, visual testing, and anomaly detection using AI/ML.
- Awareness of responsible AI usage in testing — reliability, traceability, and validation of AI-generated artifacts.
Quality Engineering & Practices
- Strong understanding of shift-left, shift-right, TDD/BDD, contract testing, and chaos/resilience testing.
- Experience with performance testing (JMeter, Gatling, k6) and non-functional testing strategies.
- Familiarity with observability tooling (Splunk, ELK, Grafana, Dynatrace) to support test analysis and production quality monitoring.
- Knowledge of test data management and synthetic data generation, including AI-assisted approaches.
Business Domain Knowledge
- Understanding of financial services domains — ideally counterparty risk, security services, CPA/CDA , or similar regulated, workflow-driven environments.
- Awareness of compliance, audit, and data sensitivity considerations that shape testing strategies in financial systems.
Leadership & Collaboration
- Demonstrated ability to lead large-scale quality transformations and influence engineering culture.
- Strong communication skills to engage engineering leaders, product owners, and business stakeholders.
- Experience mentoring QA engineers and building high-performing quality engineering teams.
Engineering Mindset
- Quality as a Product: Treat quality as a first-class deliverable, not a phase — engineered into design, code, and operations.
- AI-First, Pragmatic Mindset: Apply AI where it measurably improves speed, coverage, or reliability — avoid AI for its own sake.
- Automation-First, Manual-Where-It-Matters: Automate aggressively, but preserve human judgment for exploratory, UX, and risk-based testing.
- Shift-Left & Prevention-Oriented: Focus on preventing defects rather than just finding them.
- Data-Driven Decisions: Use metrics, telemetry, and AI insights to drive continuous improvement in quality and delivery.
- Production-Grade Discipline: Build resilient, maintainable test assets with the same rigor as production code.
- Continuous Learning: Stay current with evolving AI tools and quality engineering practices, and bring proven ideas into the team.
- Ownership & Accountability: Take end-to-end responsibility for release quality — from design through post-production monitoring.
- Collaboration Over Silos: Work transparently across development, product, and business teams to deliver trustworthy, high-quality software.
How we’ll support you
- Training and development to help you excel in your career.
- Coaching and support from experts in your team.
- A culture of continuous learning to aid progression.
- A range of flexible benefits that you can tailor to suit your needs.
About us and our teams
Please visit our company website for further information:
https://www.db.com/company/company.html
We strive for a culture in which we are empowered to excel together every day. This includes acting responsibly, thinking commercially, taking initiative and working collaboratively.
Together we share and celebrate the successes of our people. Together we are Deutsche Bank Group.
We welcome applications from all people and promote a positive, fair and inclusive work environment.