Career Level 7 | Test Architecture •Automation • GenAI• Agentic Testing

Summary

We are looking for a hands-on Quality Engineering Manager to lead engineering teams delivering high-quality, reliable, AI-augmented applications across complex, cloud-native environments. This is a leadership role for a mature practitioner who is equally comfortable architecting a test strategy, coaching senior engineers, and standing in front of a client leadership team to make the case for change.

AI is fundamentally changing how quality engineering is practiced — and we are already in that shift. In this role, you will own and advance our QE capability: modernizing automation, embedding GenAI and agentic testing with the right human oversight, and positioning quality as a strategic partner to engineering and product — not a downstream gatekeeper.

Roles & Responsibilities

Lead modern quality engineering delivery

- Lead and actively contribute to functional, integration, end-to-end, performance, and resilience testing across distributed, cloud-native, and AI-enabled systems.
- Own the end-to-end quality strategy for multiple concurrent workstreams — from requirements review and test architecture through release readiness and production quality signals.
- Stay hands-on where it matters most: code review of critical automation, root-cause analysis of complex defects, and design of the hardest test scenarios.

Drive AI-native automation and agentic testing

- Champion adoption of GenAI-assisted test authoring — LLM-based test case generation from user stories and acceptance criteria, AI-generated synthetic test data, visual regression, and self-healing UI automation.
- Introduce agentic testing patterns — autonomous agents that plan, generate, execute, and analyze tests; multi-agent orchestration across the SDLC; and explicit guardrails for what agents decide vs. what humans approve.




- Establish human-in-the-loop review gates so AI-generated tests are curated by experienced engineers before they become part of the trusted suite — capturing the productivity gains without inheriting hallucinated or low-value coverage.
- Modernize automation frameworks (Playwright, Cypress, Selenium, Appium, REST-assured, Pact, k6/Gatling) and integrate them into CI/CD pipelines with quality gates that support continuous, shift-left, and shift-right testing.

Test agentic and GenAI-powered solutions

- Define test strategies for AI-native features where behavior is non-deterministic — identifying what to assert, how to handle variability across runs, and where human judgement must remain in the evaluation loop.
- Design test coverage for agentic workflows and multi-step AI interactions — including task completion, tool invocation accuracy, decision branching, and failure and fallback behavior across agent chains.
- Establish regression and monitoring approaches for AI outputs — detecting drift, degradation, and unexpected behavior changes as models or prompts evolve in production.
- Work with development and product teams to define acceptance criteria for AI features — translating product intent into testable conditions and building shared understanding of what good looks like when the output is never identical twice.

Lead the team and deliver across engagements

- Serve as technical mentor and quality coach — developing engineers on modern QE practices, AI-assisted testing, and risk-based prioritization; building a team that holds high standards without being a bottleneck.




- Lead or contribute to practice-wide QE initiatives — driving internal capability programmes, tooling standardization, or communities of practice that raise the quality bar across teams and accounts.
- Embed into client programmes and delivery teams — strengthening QE capability in existing engagements, leading UAT planning and execution with client stakeholders, or taking delivery ownership of testing on active programmes.
- Partner with product, engineering, and business stakeholders to embed quality as a shared responsibility — shifting the perception of QE from downstream checkpoint to active participant in design, build, and release decisions.

Professional & Technical Skills

Must-have

- Deep, hands-on experience in functional, integration, end-to-end, and non-functional testing across multi-system, distributed, cloud-native applications.
- Practical experience applying GenAI to testing — LLM-driven test case generation, self-healing scripts, synthetic test data, visual/AI-based regression, and defect clustering. Comfortable with prompt engineering as a testing skill.
- Working knowledge of agentic AI in QE — autonomous test agents, multi-agent orchestration, and the review-gate patterns that make agent-generated coverage trustworthy.
- Fluency in shift-left and shift-right practices — quality-by-design in requirements and architecture; production telemetry, chaos, and observability-driven testing on the right extending into CI/CD execution models.
- Strong leadership at manager level — mentoring senior engineers, running quality communities of practice, hiring, and delivering multi-team transformation programs.
- Excellent stakeholder communication — able to translate quality data into business risk, hold the line on quality standards with senior clients, and drive change through influence rather than authority.

📌 Quality Engineer (Bengaluru)
🏢 Accenture in India
📍 Bengaluru