Senior QA Engineer / AI-Driven Test Automation Lead, Counterparty Dashboard, Security Service, CPA/CDA, VP
Deutsche Bank · Pune · 12+ yrs experience · Posted 2026-07-18
Tech stack: Azure, GitHub Actions, Jenkins, Kafka, Python, TypeScript
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About the role
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. You will set standards for automation, ensure high-quality releases, and drive a shift towards
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.
Qualifications:
- 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
- (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.
Qualifications
- 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 (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.
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.