QE Solution Director
Coforge
•
Houston, TX
0
0
0
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Not Applicable
Posted April 4, 2026
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Responsibilities
Responsibilities
- AI-Enabled QE Solutioning (Primary Charter)
- Architect AI-first QE solutions leveraging GHCP, Generative AI, ML-based defect prediction, autonomous test generation, and intelligent test data creation.
- Define AI use cases across the entire QE lifecycle—test design automation, risk-based optimization, impact analytics, and continuous validation.
- Build reusable AI accelerators, prompts, copilots, templates , and solution kits to differentiate QE offerings.
- Evaluate and recommend best-fit AI/QE platforms for client ecosystems including GHCP, Azure OpenAI, Selenium, Playwright, Tricentis, Katalon, and cloud-native DevOps stacks.
- Establish governance for responsible AI usage in QE.
- Strategic Projects, Pilots & Account Mining
- Lead AI pilots and proof-of-value (PoV) initiatives to demonstrate measurable impact—cycle time reduction, automation uplift, defect leakage reduction, and cost efficiency.
- Drive cross-account AI adoption by identifying areas for modernization, automation, and AI-led productivity improvements.
- Shape new opportunities within accounts through strategic programs , capability showcases, and client workshops.
- Develop account-specific AI roadmaps , maturity models, and transformation charters.
- 3.
- Solutioning & Pre-Sales Leadership
- Own QE solutioning for RFPs, RFIs, and proposals , including estimation, delivery models, staffing, and differentiators.
- Create compelling value narratives highlighting AI-enabled acceleration, automation efficiency, and quality cost reduction .
- Represent QE in orals, client demos, and AI capability walk-throughs.
- Build scalable solution blueprints that integrate functional, automation, performance, security, data, and AI-driven validation.
- 4.
- Quality Engineering Leadership
- Provide architectural direction across Functional QA, UI/API automation, Performance, Security, and AI-led QE .
- Recommend enterprise-grade QE toolchains optimized for ERP, CRM, API-led, and cloud-native digital ecosystems.
- Drive QE modernization by introducing self-healing automation, autonomous test generation, shift-left testing , and DevOps‑integrated quality gates.
- 5.
- Collaboration, Governance & Delivery Alignment
- Work with Delivery, DevOps, Engineering, and Enterprise Architecture to ensure solution feasibility and adoption .
- Ensure seamless transition from solution to delivery including guardrails, scope clarity, and quality governance.
- Align solutions with organizational cost models, margin expectations, and client value realization frameworks.
Not Met Priorities
What still needs stronger evidence
Requirements
- Experience: 14 + Years.
- 2.
- 12–15 years in QE; 3+ years in QE Architecture, AI-led QE, Solutioning, or Pre-Sales .
- Strong expertise with GHCP (GitHub Copilot), Azure OpenAI, AI-based test generation , and enterprise automation frameworks.
- Demonstrated experience leading client-facing AI pilots/PoVs .
- Ability to create high-quality proposal content—estimates, assumptions, solution writeups, value metrics.
- Excellent communication, storytelling, and stakeholder influence skills.
- Experience working with bid teams and large transformation programs.
Preferred Skills
- Experience with AI/QE in ERP (SAP/Oracle/NetSuite), CRM, MuleSoft/API-led integrations , and cloud modernization programs.
- exposure to performance engineering, application security, and DevOps pipelines.
- Certifications: ISTQB, Agile, AWS/Azure, DevOps, GitHub, or AI certifications.
Role : QE Solution Director
Skills : GHCP (GitHub Copilot), Azure OpenAI, AI-based test generation
Experience: 14 + Years.
Location : Houston TX. Role Summary We are seeking an AI-Driven Quality Engineering (QE) Solution Architect to lead the design and rollout of next‑generation, AI-enabled QE solutions powered by platforms such as GitHub Copilot (GHCP) , Azure OpenAI, and intelligent automation toolchains. This role will drive RFP/proposal solutioning , lead strategic AI pilots , and deliver tangible quality, velocity, and cost benefits that accelerate client adoption of AI across the account. The architect will work closely with Sales, Delivery, DevOps, and Enterprise Architecture to position differentiated, automation-first QE solutions and enable account mining. Key Responsibilities: 1. AI-Enabled QE Solutioning (Primary Charter)
Architect AI-first QE solutions leveraging GHCP, Generative AI, ML-based defect prediction, autonomous test generation, and intelligent test data creation.
Define AI use cases across the entire QE lifecycle—test design automation, risk-based optimization, impact analytics, and continuous validation.
Build reusable AI accelerators, prompts, copilots, templates , and solution kits to differentiate QE offerings.
Evaluate and recommend best-fit AI/QE platforms for client ecosystems including GHCP, Azure OpenAI, Selenium, Playwright, Tricentis, Katalon, and cloud-native DevOps stacks.
Establish governance for responsible AI usage in QE. 2. Strategic Projects, Pilots & Account Mining
Lead AI pilots and proof-of-value (PoV) initiatives to demonstrate measurable impact—cycle time reduction, automation uplift, defect leakage reduction, and cost efficiency.
Drive cross-account AI adoption by identifying areas for modernization, automation, and AI-led productivity improvements.
Shape new opportunities within accounts through strategic programs , capability showcases, and client workshops.
Develop account-specific AI roadmaps , maturity models, and transformation charters. 3. Solutioning & Pre-Sales Leadership
Own QE solutioning for RFPs, RFIs, and proposals , including estimation, delivery models, staffing, and differentiators.
Create compelling value narratives highlighting AI-enabled acceleration, automation efficiency, and quality cost reduction .
Represent QE in orals, client demos, and AI capability walk-throughs.
Build scalable solution blueprints that integrate functional, automation, performance, security, data, and AI-driven validation. 4. Quality Engineering Leadership
Provide architectural direction across Functional QA, UI/API automation, Performance, Security, and AI-led QE .
Recommend enterprise-grade QE toolchains optimized for ERP, CRM, API-led, and cloud-native digital ecosystems.
Drive QE modernization by introducing self-healing automation, autonomous test generation, shift-left testing , and DevOps‑integrated quality gates. 5. Collaboration, Governance & Delivery Alignment
Work with Delivery, DevOps, Engineering, and Enterprise Architecture to ensure solution feasibility and adoption .
Ensure seamless transition from solution to delivery including guardrails, scope clarity, and quality governance.
Align solutions with organizational cost models, margin expectations, and client value realization frameworks. Required Skills & Experience
12–15 years in QE; 3+ years in QE Architecture, AI-led QE, Solutioning, or Pre-Sales .
Strong expertise with GHCP (GitHub Copilot), Azure OpenAI, AI-based test generation , and enterprise automation frameworks.
Demonstrated experience leading client-facing AI pilots/PoVs .
Ability to create high-quality proposal content—estimates, assumptions, solution writeups, value metrics.
Excellent communication, storytelling, and stakeholder influence skills.
Experience working with bid teams and large transformation programs. Preferred Skills / Certifications
Experience with AI/QE in ERP (SAP/Oracle/NetSuite), CRM, MuleSoft/API-led integrations , and cloud modernization programs.
exposure to performance engineering, application security, and DevOps pipelines.
Certifications: ISTQB, Agile, AWS/Azure, DevOps, GitHub, or AI certifications.
Skills : GHCP (GitHub Copilot), Azure OpenAI, AI-based test generation
Experience: 14 + Years.
Location : Houston TX. Role Summary We are seeking an AI-Driven Quality Engineering (QE) Solution Architect to lead the design and rollout of next‑generation, AI-enabled QE solutions powered by platforms such as GitHub Copilot (GHCP) , Azure OpenAI, and intelligent automation toolchains. This role will drive RFP/proposal solutioning , lead strategic AI pilots , and deliver tangible quality, velocity, and cost benefits that accelerate client adoption of AI across the account. The architect will work closely with Sales, Delivery, DevOps, and Enterprise Architecture to position differentiated, automation-first QE solutions and enable account mining. Key Responsibilities: 1. AI-Enabled QE Solutioning (Primary Charter)
Architect AI-first QE solutions leveraging GHCP, Generative AI, ML-based defect prediction, autonomous test generation, and intelligent test data creation.
Define AI use cases across the entire QE lifecycle—test design automation, risk-based optimization, impact analytics, and continuous validation.
Build reusable AI accelerators, prompts, copilots, templates , and solution kits to differentiate QE offerings.
Evaluate and recommend best-fit AI/QE platforms for client ecosystems including GHCP, Azure OpenAI, Selenium, Playwright, Tricentis, Katalon, and cloud-native DevOps stacks.
Establish governance for responsible AI usage in QE. 2. Strategic Projects, Pilots & Account Mining
Lead AI pilots and proof-of-value (PoV) initiatives to demonstrate measurable impact—cycle time reduction, automation uplift, defect leakage reduction, and cost efficiency.
Drive cross-account AI adoption by identifying areas for modernization, automation, and AI-led productivity improvements.
Shape new opportunities within accounts through strategic programs , capability showcases, and client workshops.
Develop account-specific AI roadmaps , maturity models, and transformation charters. 3. Solutioning & Pre-Sales Leadership
Own QE solutioning for RFPs, RFIs, and proposals , including estimation, delivery models, staffing, and differentiators.
Create compelling value narratives highlighting AI-enabled acceleration, automation efficiency, and quality cost reduction .
Represent QE in orals, client demos, and AI capability walk-throughs.
Build scalable solution blueprints that integrate functional, automation, performance, security, data, and AI-driven validation. 4. Quality Engineering Leadership
Provide architectural direction across Functional QA, UI/API automation, Performance, Security, and AI-led QE .
Recommend enterprise-grade QE toolchains optimized for ERP, CRM, API-led, and cloud-native digital ecosystems.
Drive QE modernization by introducing self-healing automation, autonomous test generation, shift-left testing , and DevOps‑integrated quality gates. 5. Collaboration, Governance & Delivery Alignment
Work with Delivery, DevOps, Engineering, and Enterprise Architecture to ensure solution feasibility and adoption .
Ensure seamless transition from solution to delivery including guardrails, scope clarity, and quality governance.
Align solutions with organizational cost models, margin expectations, and client value realization frameworks. Required Skills & Experience
12–15 years in QE; 3+ years in QE Architecture, AI-led QE, Solutioning, or Pre-Sales .
Strong expertise with GHCP (GitHub Copilot), Azure OpenAI, AI-based test generation , and enterprise automation frameworks.
Demonstrated experience leading client-facing AI pilots/PoVs .
Ability to create high-quality proposal content—estimates, assumptions, solution writeups, value metrics.
Excellent communication, storytelling, and stakeholder influence skills.
Experience working with bid teams and large transformation programs. Preferred Skills / Certifications
Experience with AI/QE in ERP (SAP/Oracle/NetSuite), CRM, MuleSoft/API-led integrations , and cloud modernization programs.
exposure to performance engineering, application security, and DevOps pipelines.
Certifications: ISTQB, Agile, AWS/Azure, DevOps, GitHub, or AI certifications.