Position Title: QA AI Strategist
Duration: Direct Hire
Location: Remote, coming onsite a few times monthly
Start Date: September
ROLE OVERVIEW
Seeking a senior, hands-on QA AI Strategist to define, build, and scale AI-enabled quality-engineering capabilities across the organization and its key client accounts.
This role combines deep quality-engineering expertise, applied AI/ML and generative AI knowledge, technical solution architecture, executive consulting, pre-sales leadership, and practice development. The QA AI Strategist will research emerging technologies, evaluate platforms, create proofs of concept, design reusable architectures and accelerators, support client delivery, and help position client as a leader in AI-driven quality engineering.
This is not a traditional QA Manager or high-level strategy-only position. The successful candidate must be capable of personally evaluating technologies, designing technical solutions, developing proofs of concept, creating compelling demonstrations, and advising delivery teams. This individual must also be comfortable presenting an AI quality-engineering vision, business case, and solution roadmap to C-suite stakeholders and prospective clients.
The QA AI Strategist will collaborate with QA practice leaders, sales and account teams, technology partners, and subject-matter experts across functional testing, automation, performance, mobile, data/ETL, accessibility, and security.
REQUIRED QUALIFICATIONS
· Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related discipline.
· At least 15 years of experience in QA, software testing, test automation, quality engineering, or related technology disciplines.
· Significant experience with modern QA practices, test automation, Agile delivery, DevOps, and CI/CD.
· Recent hands-on experience applying AI/ML and/or generative AI to software quality or quality-engineering workflows.
· Experience with relevant applications such as AI-generated testing, intelligent automation, code analysis, defect prediction or clustering, NLP-based testing, LLM-powered assistants, test optimization, or AI-supported quality analysis.
· Strong knowledge of QA tools, automation frameworks, delivery pipelines, testing disciplines, and quality-engineering ecosystems.
· Familiarity with AI platforms and services, including cloud-based AI services, LLMs, generative AI platforms, machine-learning frameworks, and/or AI agents.
· Demonstrated ability to design technical solutions and reference architectures that integrate AI into QA toolchains and delivery pipelines.
· Experience personally contributing to technical evaluations, prototypes, proofs of concept, accelerators, or demonstrations.
· Experience establishing appropriate security, privacy, governance, and responsible-AI controls.
· Experience supporting pre-sales and delivery activities, including discovery, solutioning, estimation, proposals, RFP/RFI responses, proofs of concept, demonstrations, and executive presentations.
· Ability to identify business challenges, develop a strategic AI solution, quantify its value, and communicate the vision to executive stakeholders.
· Strong consulting skills, including problem structuring, market analysis, technical analysis, synthesis, facilitation, and recommendation development.
· Excellent written and verbal English communication skills.
· Ability to collaborate across practice leadership, architecture, engineering, QA, sales, marketing, account teams, clients, and technology partners.
· Analytical and data-driven approach with a focus on measurable outcomes and ROI.
· Residence within reasonable commuting distance of an approved office.
PREFERRED QUALIFICATIONS
· Advanced degree or relevant certifications in AI/ML, cloud technology, software quality, or quality engineering.
· ISTQB, cloud, generative AI, machine-learning, or relevant platform certifications.
· Experience building or scaling an AI-enabled quality-engineering capability within a consultancy, systems integrator, technology company, or large enterprise.
· Experience creating reusable intellectual property, technical accelerators, assessment tools, or reference architectures.
· Experience developing thought-leadership content and supporting go-to-market campaigns.
· Experience managing relationships with AI, cloud, and testing-tool vendors.
· Experience supporting co-selling, strategic alliances, or partner-led opportunities.
· Experience designing AI solutions for highly regulated or security-sensitive clients.
· Familiarity with Model Context Protocol, retrieval-augmented generation, agentic workflows, vector databases, AI evaluation frameworks, prompt engineering, and LLM observability.
· Experience measuring the operational and financial impact of AI-enabled QA solutions.
KEY RESPONSIBILITIES
AI Research, Market Analysis, and Benchmarking
· Continuously evaluate emerging AI, machine-learning, and generative AI technologies relevant to software quality and quality engineering.
· Research capabilities such as AI-generated test cases, intelligent test-data generation, defect prediction, defect clustering, regression optimization, code analysis, self-healing automation, risk-based testing, and LLM-powered QA assistants.
· Conduct structured competitive analyses, technical evaluations, proofs of concept, and benchmark studies of AI-powered QA products, platforms, models, and frameworks.
· Compare potential solutions across technical capability, functional fit, integration effort, scalability, security, compliance, cost, risk, and expected business value.
· Produce clear evaluation reports, decision frameworks, and recommendations for internal leaders and clients.
· Track relevant market trends, standards, research, vendor roadmaps, and industry best practices.
· Separate practical, production-ready capabilities from emerging technologies that have not yet demonstrated measurable value.
AI-Enabled QA Solution Architecture
· Design end-to-end AI-enabled QA solutions and reference architectures that integrate with client platforms, testing tools, data environments, and delivery pipelines.
· Define technical patterns, APIs, data pipelines, model interactions, orchestration approaches, and integration models for incorporating AI into QA workflows.
· Design solutions using generative AI, large language models, traditional machine learning, cloud AI services, intelligent agents, and AI-assisted automation.
· Collaborate with QA subject-matter experts to apply AI across functional testing, automation, mobile, accessibility, performance, security, and data/ETL testing.
· Identify opportunities to improve test-case generation, test-data management, defect analysis, regression selection, test maintenance, code quality, risk prediction, and release decisions.
· Ensure proposed solutions are practical, scalable, reusable, secure, supportable, and aligned with real delivery requirements.
· Establish measurable success criteria, quality metrics, and ROI models for AI-enabled QA implementations.
Hands-On Prototyping and Accelerator Development
· Personally contribute to the design and development of proofs of concept, technical prototypes, reusable assets, accelerators, and demonstration environments.
· Translate strategic ideas into working solutions that can be evaluated by internal stakeholders, clients, and prospective clients.
· Work with engineers and architects to integrate AI services, models, tools, APIs, test frameworks, and CI/CD pipelines.
· Evaluate model outputs, solution accuracy, reliability, scalability, maintainability, security, and cost.
· Document technical patterns and implementation guidance so successful concepts can be reused across accounts.
· Provide technical leadership and troubleshooting support during implementation and client delivery.
Responsible AI, Governance, and Risk
· Establish standards and guardrails for the responsible, secure, compliant, and transparent use of AI within QA processes.
· Address concerns involving data privacy, intellectual property, security, model accuracy, hallucinations, bias, explainability, human oversight, and regulatory requirements.
· Define appropriate controls for prompts, training or reference data, model outputs, automated decisions, and third-party AI tools.
· Partner with security, legal, compliance, data, engineering, and client stakeholders when evaluating or implementing AI QA solutions.
· Ensure AI-enabled processes include appropriate human validation and do not introduce unacceptable delivery or business risk.
· Develop governance frameworks that support experimentation while protecting client data and production environments.
Sales, Pre-Sales, and Delivery Support
· Partner with sales, account, and practice leadership to identify, qualify, and shape AI-enabled QA opportunities.
· Diagnose client business and technology challenges and translate them into differentiated AI QA strategies and solutions.
· Lead or support discovery sessions, solution design, staffing approaches, estimates, proposals, and RFP/RFI responses.
· Create and deliver persuasive, business-oriented presentations and demonstrations that communicate value, ROI, feasibility, and competitive differentiation.
· Present AI QA strategies and investment cases to C-suite executives, technology leaders, and prospective clients.
· Support account teams in moving opportunities from initial concept through proposal, proof of concept, pilot, and scaled implementation.
· Advise technical architects, QA leaders, and development teams during delivery.
· Help resolve complex technical, architectural, adoption, and governance challenges.
· Use feedback and lessons from client engagements to improve broader AI QA offerings.
Use Cases, Assets, and Thought Leadership
· Identify high-impact AI QA use cases across industries, platforms, technologies, and testing disciplines.
· Prioritize use cases based on business value, technical feasibility, repeatability, implementation effort, risk, and market demand.
· Develop reusable solutions, playbooks, reference architectures, assessment frameworks, demos, and accelerators.
· Produce technical and business-friendly materials, including executive presentations, one-page summaries, solution briefs, white papers, blogs, videos, and implementation guides.
· Collaborate with marketing and practice leadership to communicate AI QA vision and capabilities.
· Represent the practice in internal events, client workshops, webinars, partner programs, and industry discussions.
Partnerships and Technology Ecosystem
· Develop relationships with strategic AI, cloud, quality-engineering, and testing-tool providers.
· Evaluate partnership opportunities that could strengthen AI QA offerings and go-to-market strategy.
· Monitor partner technologies and roadmaps and assess their relevance to client needs.
· Coordinate joint proofs of concept, co-selling, co-marketing, webinars, pilots, training, and enablement activities.
· Provide informed recommendations regarding vendor selection, strategic alliances, and technology investments.
· Advocate for capabilities and roadmap improvements that address client requirements.
Talent Strategy and Community Development
· Help define the roles, skills, competencies, and career paths required to deliver AI-enabled quality engineering.
· Support the development of hiring profiles for positions such as AI QA Engineer, AI Test Architect, and AI Quality Strategist.
· Partner with QA practice leaders to create training curricula, certification paths, technical assessments, and learning programs.
· Mentor QA engineers, leads, architects, and consultants on applied AI concepts, technologies, solution patterns, and governance.
· Lead workshops, demonstrations, internal enablement sessions, and knowledge-sharing initiatives.
· Build an active internal AI QA community that promotes responsible experimentation, technical excellence, collaboration, and reuse.
Consulting and Stakeholder Leadership
· Serve as a trusted advisor to QA practice leaders, engagement leaders, account teams, technology executives, and client stakeholders.
· Lead structured conversations that clarify business objectives, technical environments, constraints, risks, expected outcomes, and investment requirements.
· Translate complex AI and quality-engineering concepts into understandable business recommendations.
· Facilitate alignment among technical, business, sales, marketing, delivery, security, and partner stakeholders.
· Present clear, evidence-based recommendations supported by technical findings, market analysis, data, and measurable outcomes.
· Build credibility with both hands-on technical teams and senior executive audiences.
SUCCESS IN THIS ROLE
The successful candidate will operate across five connected areas:
1. Strategy: Define vision, roadmap, and market position for AI-enabled quality engineering.
2. Technology: Design architectures and personally contribute to evaluations, prototypes, proofs of concept, and reusable solutions.
3. Consulting: Diagnose business problems and advise senior client and practice stakeholders.
4. Growth: Shape opportunities, support pursuits, create compelling demonstrations, and sell the value of the solution.
5. Enablement: Develop assets, partnerships, skills, and communities that allow AI QA capabilities to scale across accounts.
This role is best suited for a senior QA technology strategist who can convert emerging AI capabilities into practical, secure, marketable, and measurable quality-engineering solutions. Candidates who are exclusively people managers, traditional QA delivery leads, high-level strategists without technical depth, or general AI professionals without significant QA expertise will not be a fit.
About Us
ECCO Select is certified as a Women-owned, Minority-owned, Small Business Enterprise. We are a talent acquisition and advisory consulting company, specializing in providing people, process, and technology solutions for our clients’ needs. ECCO Select has experience in assisting our commercial and government clients successfully manage projects and programs that transform their business operations through a variety of IT solutions. We’re the talent behind the technology. To find out more about ECCO visit www.eccoselect.com.
Our Commitment
We would love to have you join our team! ECCO Select is committed to hiring and retaining a diverse workforce. ECCO Select’s policy is to provide equal opportunity to all people without regard to race, color, religion, national origin, ancestry, marital status, veteran status, age, disability, pregnancy, genetic information, citizenship status, sex, sexual orientation, gender identity or any other legally protected category.
Equal Employment Opportunity is The Law
This Organization Participates in E-Verify
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Vision insurance
Application Question(s):
- Can you work as a W2 employee without any sponsorship or Visa transfer?
- Do you have familiarity with AI platforms including AI services, LLMs, GenAI platforms, machine learning frameworks and/or AI agents?
- Do you have demonstrated experience designing technical solutions that integrate AI into QA toolchains and delivery pipelines?
- Are you interested in a strategy role that focuses on converting AI capabilities into practical quality engineering solutions?
- Can you work primarily remote coming onsite a few times monthly to an office in your region?
- Do you have 15 or more years of experience in QA, software testing, test automation, or quality engineering?
- Are you experienced with modern QA practices?
Work Location: Remote
