Quality Engineer

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 clear 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.

Work arrangement
Hybrid