QE Lead

CGI•Pittsburgh, PA

19h•Onsite

About The Position

CGI is looking for an experienced Quality Engineering Lead to join our Enterprise Document Management team, supporting our client which is a large US Bank, working in an advanced technology environment. The Senior Quality Engineering Lead is responsible for defining and driving the quality strategy, validation standards, and release readiness for the Document Management platform, focused on user-centric application capabilities and integration across existing enterprise systems. This role establishes quality frameworks, automation strategies, and validation models across document access, metadata integrity, search (including vector-based semantic retrieval), and governance controls, forming the foundation for AI-powered search and agentic workflows. The candidate for this role will be expected to be onsite five days a week at our client site in Pittsburgh PA. For this role on this particular client engagement, employer sponsorship of immigration related visa and/or green card status as part of the PERM process will not be available.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent practical experience
6–10 years of experience in quality engineering, software testing, or enterprise platform validation
3+ years leading quality strategy or providing technical leadership on large-scale delivery programs
Proven experience validating document management systems, enterprise search platforms, or metadata-driven applications
Strong expertise in test strategy design, quality gates, and automated validation frameworks
Experience testing API-driven, microservices, and event-driven architectures
Hands-on experience with Microsoft Azure, CI/CD pipelines, and test automation frameworks
Strong understanding of: Search relevance and metadata validation, Vector/semantic search and AI-driven retrieval validation
Experience validating security, RBAC, and compliance controls in regulated environments
Familiarity with AI/ML, LLM-based retrieval, or agentic workflows
Strong communication skills with ability to present quality metrics, risks, and release readiness to stakeholders

Responsibilities

Define and own the end-to-end quality strategy, standards, and release governance
Establish quality gates, automation frameworks, and validation metrics across application and integration layers
Partner with Engineering, Product, and Architecture to drive shift-left quality practices and early defect prevention
Enable data-driven release decisions, providing clear visibility into quality metrics, risks, and readiness
Provide technical leadership and oversight across QE practices, automation, and continuous improvement
Define and lead end-to-end validation strategies for document management workflows, including document submission, access, and retrieval across systems
Establish validation frameworks for search and retrieval capabilities, including: Metadata-driven and full-text search, Vector-based semantic search (embedding similarity, ranking accuracy, relevance tuning), Entitlement enforcement and context-aware results
Define and govern validation for metadata quality, taxonomy alignment, and data integrity across systems
Ensure document lifecycle and governance validation, including retention, auditability, traceability, and compliance enforcement
Lead validation of AI-powered retrieval and agentic workflows, ensuring accuracy, grounding, and consistency
Define and oversee validation of federated integrations, ensuring consistency in document access, metadata alignment, and cross-system behavior
Establish testing strategies for API and event-driven integrations, including data integrity, resilience, retry handling, and failure scenarios
Ensure end-to-end traceability, auditability, and regulatory compliance across application and integration layers
Define and oversee security and access control validation (RBAC) across all document and retrieval scenarios
Lead performance and scalability testing strategies for search, retrieval, and integration workloads
Drive automation-first quality practices, including CI/CD-integrated test pipelines and continuous validation
Establish quality observability, including dashboards, reporting, and actionable insights on coverage, defects, and system performance