Practice Architect-Test Lead Lead

TEKsystems•Austin, TX

1d•$160,000 - $225,000•Remote

About The Position

Think of TEKsystems Global Services (TGS) as the growth solution for enterprises today. We unleash growth through technology, strategy, design, execution and operations with a customer-first mindset for bold business leaders. We deliver cloud, data and customer experience solutions. Our partnerships with leading cloud, design and business intelligence platforms fuel our expertise. We value deep relationships, dedication to serving others and inclusion. We drive positive outcomes for our people and our business, and we stay true to our commitments and act in harmony with our words. We exist to create significant opportunity for people to achieve fulfillment through career success. Ready to join us? Here’s what the opportunity supported through our TGS Talent Acquisition Team requires: Practice: Quality Engineering / AI & Data Quality TEKsystems Global Services is an information technology services company with decades of experience delivering solutions in Application Development, Quality Engineering, Data, AI, and managed services across industries including Financial Services, Healthcare, Energy, Mobility, and Life Sciences. As part of our continued growth, the Quality Engineering Practice is seeking an AI Test Lead Architect to define, build, and scale our enterprise AI testing capabilities. This role offers the opportunity to shape the future of AI quality engineering, work with industry leading clients, and establish market differentiated AI testing services and accelerators. Role Description The AI Test Lead Architect is responsible for the strategy, vision, architecture, execution, and growth of the AI Testing Practice. This role maintains a bird’s eye view of AI quality, encompassing technical, process, delivery, governance, and business considerations across AI enabled systems. Acting as the “Keeper of the Vision” for AI quality, the AI Test Practice Lead defines testing methodologies, frameworks, accelerators, and operating models that ensure trust, reliability, performance, and compliance of AI systems across the SDLC. The role interfaces with all levels of the organization—from C-level executives to hands-on team members delivering software. The AI Test Lead Architect will provides architectural guidance, mentoring, client advisory, and escalation support for complex AI quality challenges.

Requirements

Proven experience designing and implementing AI runtime testing methodologies.
Strong background as a Test Architect for enterprise scale systems.
Demonstrated success delivering AI testing accelerators across SDLC, automation, and reporting.
Experience leading and scaling testing teams and practices.
Hands on experience with AI/ML and GenAI quality concepts (training, inference, drift, bias, explainability).
Familiar with collecting and analysis of Precision, Recall, F1 Scoring, and Confusion Matrix
Shift left testing and continuous quality engineering.
Agile, Waterfall, and hybrid delivery models.
CI/CD and MLOps integration.
Test automation frameworks and data validation strategies.
Monitoring, observability, and AI system reporting.
Excellent communication skills with both technical and non technical audiences.
Ability to influence decisions at engineering, product, risk, and executive levels.
Strong presentation and workshop facilitation skills.
Consultative mindset focused on outcomes, risk reduction, and business value.
Comfortable working in open, dynamic, and collaborative team environments.
Strong analytical, problem solving, and systems thinking abilities.
Self starter with a proactive leadership mindset.
Strong time management and organizational skills.
Passionate advocate for quality, trust, and responsible AI.
Desire to continuously improve AI quality processes and practices.
Minimum 12 years of experience in Quality Engineering, Testing, or Software Engineering.
Minimum 8 years in senior technical or architecture roles.
3–5 years of experience leading teams or practices.
Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
Candidates must demonstrate strong understanding of business processes, AI system risks, and enterprise delivery models.
The ideal candidate quickly grasps complex concepts, communicates effectively at all levels, and balances technical depth with strategic thinking.

Responsibilities

Define, build, and scale the AI Test Practice, including standards, playbooks, accelerators, and delivery models.
Establish a clear practice vision, roadmap, and KPIs aligned with business outcomes and client needs.
Act as the senior escalation point for AI quality, risk, and architecture decisions.
Identify internal process and technology gaps as well as provide R&D direction to address them.
Support hiring, mentoring, and career development across AI testing and quality engineering teams.
Design and implement AI runtime testing methodologies for deterministic and non deterministic systems (ML models, GenAI, LLMs).
Define AI test architecture addressing: -Data quality and lineage -Model behavior and performance -Bias, fairness, and explainability -Drift, robustness, and security
Develop and guide shift-left AI testing through early collaboration, strategic conversations, and shared standards that shape responsible, high-quality AI delivery from the start.
Establish AI quality gates embedded in CI/CD and MLOps workflows.
Create, evolve, and maintain AI testing accelerators across: -SDLC integration -Test automation -Runtime monitoring and observability -Risk and compliance reporting
Standardize reusable frameworks for AI regression testing, GenAI prompt validation, drift detection, and reporting.
Maintain a big picture view of frameworks, dependencies, client goals, and technology direction.
Apply AI testing strategies across Agile, Waterfall, and hybrid delivery models.
Partner with engineering, data science, and product teams to embed quality early and continuously.
Act as a client facing AI testing advisor, engaging confidently with technical and executive stakeholders.
Lead client workshops, AI quality assessments, and roadmap definition efforts.
Support solution design, proposals, presentations, demos, and proofs of value.
Define and track AI quality KPIs, including model accuracy, drift, bias indicators, coverage, and production stability.
Establish AI testing governance aligned with client, regulatory, and risk requirements.
Build and deliver executive level reporting dashboards for AI quality, risk, and compliance.
Drive continuous improvement through retrospectives, metrics analysis, and feedback loops.