Title: AI Architect (Hybrid)
Location: Houston, TX
Duration: 12 Months Contract
Interview Mode: Video
SUBMISSION REQUIREMENTS:
Need Genuine LinkedIn- NO RECENT OR MULTIPLE LINKEDIN
Must have full educational details with University Name and year of completion in resume.
WITHOUT ABOVE MENTIONED PREREQUISITE RESUME WILL BE DEEMED AS FAKE.
Job Description:
Position Overview
We are seeking a highly experienced Senior AI Architect to lead the strategy, governance, architecture, and adoption of AI capabilities across a global Quality Assurance organization.
This individual will serve as the central point of accountability for transforming individual AI and QA initiatives into a cohesive, governed, reusable, and value-driven enterprise capability. The AI Architect will partner with QA leadership, engineering teams, enterprise architecture, security, vendors, and business stakeholders to establish scalable AI standards and solutions that accelerate adoption while reducing technical, operational, and governance risk.
This is a strategic and technical leadership role requiring someone who can operate at both the enterprise architecture level and the execution/governance level, particularly within software testing, QA automation, generative AI, and AI-enabled engineering environments.
Key Responsibilities
AI Strategy & Governance
- Establish and evolve the AI vision, roadmap, operating model, and governance framework for the QA organization.
- Develop an accountability model across AI planning, governance, architecture, vendor management, innovation, and value realization.
- Create centralized processes for AI use-case intake, prioritization, risk assessment, and approval.
- Establish governance checkpoints, stage gates, KPIs, and executive reporting for AI initiatives.
- Partner with enterprise architecture, security, data, and Responsible AI teams to ensure alignment with organizational standards.
Enterprise AI Architecture
- Design enterprise AI architecture supporting software testing and QA transformation.
- Define standards and recommended patterns for:
- Large Language Models (LLMs)
- Generative AI
- AI agents and orchestration
- Knowledge sources and retrieval
- Data architecture
- APIs and integrations
- Prompt engineering
- Model evaluation and validation
- Develop reusable reference architectures and technical patterns that can be leveraged across multiple teams and vendors.
- Establish architectural review processes for new AI initiatives and solutions.
- Ensure AI solutions are scalable, interoperable, secure, supportable, and aligned with enterprise architecture standards.
QA & Software Testing Transformation
- Identify opportunities to apply AI across:
- Test design and test case generation
- Test automation
- Quality intelligence
- Defect analysis
- Release readiness
- Automation monitoring
- Reporting automation
- Knowledge agents
- Engineering productivity
- Provide technical oversight for high-impact AI initiatives within QA.
- Partner with QA engineers, automation engineers, developers, and delivery teams to transition AI concepts and proofs of concept into production-ready capabilities.
Technology & Integration Architecture
Define architecture and integration approaches across enterprise platforms including:
- Azure DevOps
- GitHub Copilot
- Microsoft Copilot
- Microsoft Copilot Studio
- Power Platform
- Power BI
- Tricentis Tosca
- UFT
- Enterprise APIs and data platforms
- Approved LLM and AI platforms
Identify opportunities to create reusable AI services, components, agents, prompts, and integration patterns rather than developing isolated point solutions.
Responsible AI, Security & Risk Management
- Establish standards for Responsible AI usage within software testing and QA.
- Define governance for:
- Human-in-the-loop controls
- Model evaluation
- AI validation
- Prompt engineering
- Security
- Compliance
- Intellectual property protection
- Data quality
- Technical oversight
- Develop AI solution readiness assessments covering scalability, security, architecture, compliance, operational readiness, and business value.
- Maintain AI risk registers, mitigation strategies, and scale/no-scale criteria.
Vendor & Technology Management
- Lead technical evaluations of AI vendors, platforms, and capabilities.
- Establish standardized vendor scorecards and evaluation criteria covering:
- Technical capability
- Architecture fit
- Security
- Integration maturity
- Delivery performance
- Reusability
- Business value
- Coordinate AI proofs of concept and vendor evaluations under a unified enterprise strategy.
- Reduce duplicated AI experimentation across teams and vendors.
- Track vendor commitments, technical gaps, risks, and follow-up actions.
Reusable AI Capabilities & Knowledge Management
- Build and maintain a catalog of reusable:
- AI architectures
- AI services
- Prompts
- Agent patterns
- Validation frameworks
- Governance templates
- Technical standards
- Establish ownership, version control, reuse criteria, and lifecycle management for AI assets.
- Preserve institutional knowledge through documentation of architecture decisions, lessons learned, solution patterns, standards, and reusable components.
Innovation & Proof-of-Concept Governance
- Establish a structured innovation pipeline for emerging AI opportunities.
- Define POC entry criteria, success metrics, architecture expectations, risk controls, and exit criteria.
- Ensure successful POCs transition into scalable solutions or reusable enterprise capabilities.
- Evaluate new AI technologies and recommend where they can provide measurable value to QA and engineering organizations.
Adoption & Change Enablement
- Develop an AI adoption framework for QA leaders, testers, automation engineers, engineering teams, and business stakeholders.
- Partner with technical teams to establish training, communication, enablement, and support strategies.
- Measure adoption and identify barriers, enhancement opportunities, and organizational feedback.
AI Value Realization
- Establish frameworks to measure the business impact of AI initiatives.
- Define baseline and ongoing metrics around:
- Productivity
- Quality improvement
- Cycle-time reduction
- Risk reduction
- Adoption
- Reuse
- Cost avoidance
- Develop executive-level AI reporting and dashboards connecting technical initiatives with measurable business outcomes.
- Conduct ongoing maturity assessments and recommend future AI investments and roadmap priorities.
Required Qualifications
- Extensive experience in AI architecture, enterprise architecture, or solution architecture roles.
- Strong hands-on understanding of Generative AI, LLMs, AI agents, orchestration, APIs, and enterprise AI integration patterns.
- Experience designing AI solutions within large, complex enterprise environments.
- Strong understanding of AI governance, Responsible AI, model evaluation, validation, security, and compliance.
- Experience developing reusable enterprise architecture patterns and frameworks.
- Strong knowledge of modern software development, QA, and testing practices.
- Experience with software testing automation and AI-enabled QA use cases.
- Ability to evaluate AI platforms, vendors, tools, and emerging technologies from both a technical and business perspective.
- Experience leading technical architecture reviews and providing oversight across multiple projects or workstreams.
- Ability to translate complex AI architecture concepts into executive-level recommendations and roadmaps.
- Strong communication skills and ability to work across executive leadership, engineering teams, architecture groups, vendors, and business stakeholders.
Preferred Technical Experience
Experience with several of the following is strongly preferred:
- Microsoft Azure / Azure AI
- Azure DevOps
- GitHub Copilot
- Microsoft Copilot
- Microsoft Copilot Studio
- Power Platform
- Power BI
- Enterprise LLM platforms
- Retrieval-Augmented Generation (RAG)
- AI agents and multi-agent architectures
- Prompt engineering
- API and integration architecture
- Tricentis Tosca
- UFT
- Test automation frameworks
- AI/ML model evaluation and validation
- Responsible AI frameworks
Ideal Candidate
The ideal candidate is not simply an AI developer or traditional QA architect. This person should be able to own the AI architecture and governance strategy across an enterprise QA organization, while still being technical enough to challenge solution designs, establish architecture standards, evaluate emerging technologies, and guide engineering teams.
They should be comfortable moving between executive strategy discussions and detailed technical architecture conversations and be capable of bringing structure and accountability to multiple AI initiatives occurring simultaneously across teams and vendors.
Expected Outcomes
Over the course of the engagement, this individual will help establish:
- A cohesive enterprise QA AI strategy and roadmap
- A standardized AI governance and operating model
- Enterprise AI architecture standards for software testing
- Responsible AI, security, validation, and human-in-the-loop controls
- A reusable catalog of AI architectures, agents, prompts, and frameworks
- Standardized vendor and technology evaluation processes
- An organized innovation and POC pipeline
- AI adoption and enablement frameworks
- Executive reporting and measurable AI value-realization metrics
Himanshu Goswami
Sr. IT Technical Recruiter
Stellent IT Phone:
Email: Himanshu.goswami
Gtalk: Himanshu.goswamiom
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