Automation Engineer SDET
Summary
Leads quality engineering initiatives by designing full-stack automation frameworks, integrating AI/ML, and consulting with clients to improve testing and digital transformation outcomes.
Role-Overview:
We are seeking a QE Architect to lead Quality Intelligence initiatives for clients across domains & regions. This role demands expertise in full-stack automation, AI/ML and strategic consulting to deliver measurable business outcomes.
Role-Overview:
We are seeking a QE Architect to lead Quality Intelligence initiatives for clients across domains & regions. This role demands expertise in full-stack automation, AI/ML and strategic consulting to deliver measurable business outcomes.
Duties and Responsibilities:
Architect and implement full-stack automation frameworks for applications (Web, Mobile, API, Database).
Drive risk-based testing, security, and performance validation for digital platforms.
Provide consulting on compliance, regulatory requirements, and digital transformation.
Participate in client consulting engagements and provide high-impact recommendations for quality transformation.
Prepare solution approaches, proposals, and technical statements of work (SoW) tailored to client needs.
Design and architect full-stack automation frameworks covering Web, Mobile, API, Digital, and Database testing.
Drive Test Data Management (TDM) strategies to ensure robust and scalable test environments.
Perform technical assessments, identify gaps, and recommend strategic improvements to client quality engineering practices.
Champion adoption of AI/ML-based quality engineering approaches, intelligent test design, predictive analytics, and self-healing automation.
Participate in client consulting engagements and provide high-impact recommendations for quality transformation.
Prepare solution approaches, proposals, and technical statements of work (SoW) tailored to client needs.
Support new business pursuits, RFPs, RFIs, and proactive proposals with strong solution leadership.
Identify, nurture, and convert potential opportunities into revenue-generating engagements.
Collaborate with sales and account teams to position Quality Engineering as a business differentiator in client conversations.
Define and track metrics that link automation and AI-led quality solutions directly to client ROI and revenue outcomes.
Mentor, coach, and grow high-performing engineering teams across the region
Foster a culture of innovation, continuous improvement, and cross-collaboration.
Partner with global practice leaders to ensure alignment and knowledge sharing across geographies.
Required Qualifications:
12+ years of experience in Software Testing, Test Automation, and Quality Engineering, with at least 5+ years in a leadership role driving regional or global practices.
Deep expertise in full-stack test automation: Web, API, Mobile/Digital, Database, and TDM solutions.
Strong exposure to AI/ML-based quality engineering practices – predictive analytics, autonomous AI for QE, self-healing automation, and intelligent test data management.
Experience in Multi-Agent Orchestration Systems across STLC, leveraging Agentic AI solutions, RAG (Retrieval-Augmented Generation), and LLMs for intelligent automation.
Ability to design and implement AI-driven workflows for user story refinement, test case generation, and automation script creation, ensuring measurable optimization and acceleration in QE processes.
Hands-on experience in setting up MCP (Model Context Protocol) servers to enable secure, scalable orchestration of AI agents across STLC phases, integrating context-aware prompts and multi-agent collaboration for QE automation.
Proven experience in technical consulting, solutioning, and assessments for large enterprise clients.
Strong background in pre-sales, RFPs, solution architecture, and business consulting.
Demonstrated ability to convert opportunities into revenue, aligning technical delivery with business outcomes.
Excellent client-facing, stakeholder management, and communication skills with the ability to influence CXO-level discussions.
Strong understanding of modern delivery models (Agile, DevOps, CI/CD, Cloud-native, Microservices).
Experience in building Center of Excellence (CoE) and driving knowledge sharing, accelerators, and IP creation.