Top 75 Lead software engineering java/python Jobs in Denton, Tx

Lead Software Test Automation Engineer | Test Architect - Agentic AI
Wolters Kluwer —Coppell, TX
- $89,600 - $157,000 a year
- Full-time
- Java
- JavaScript
- Mentoring
- Commuter assistance
- Paid parental leave
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- 5 years
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Moffatt & Nichol —Southlake, TX
- Full-time
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- Computer Science
- 5 years
- Wellness program
- Health savings account
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Senior Manager of Software Engineering - Big Data, Databricks
JPMorganChase —Plano, TX
- Full-time
- System design
- Java
- AWS
- Health insurance
- Tuition reimbursement
14d
Lead Security Engineer
JPMorganChase —Plano, TX
- Full-time
- 5 years
- Java
- APIs
- Health insurance
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Executive Director, Machine Learning & Gen AI Platforms (Home Lending)
JPMorganChase —Plano, TX
- Full-time
- Management
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Odessa (LeaseWave) Consultant
PRAXIS USA —Coppell, TX
- $19 - $25 an hour
- Full-time
- Azure
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QA Architect
ADP Test Code - WLP Test 02022023 —Northlake, TX
- $120 - $133 a year
- Full-time
- 7 years
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Technical Recruiter, Shared Services -Recruiting
ServiceLink —Plano, TX
- $80,000 - $100,000 a year
- Full-time
- Hybrid work
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Forward Deployed Engineer (FDE) - Remote
UnitedHealth Group —United States
- $120,100 - $214,500 a year
- Full-time
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- Azure
- Computer Science
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Lead Software Engineer - Reliability & Support
JPMorganChase —Plano, TX
- Full-time
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Lead Software Test Automation Engineer | Test Architect - Agentic AI

Wolters Kluwer -
Coppell, TX

Apply Now

Job Details

Full-time
$89,600 - $157,000 a year

Benefits

Commuter assistance
Paid parental leave
Health savings account
Health insurance
Dental insurance
401(k)
Flexible spending account
Tuition reimbursement
Parental leave
Vision insurance

Qualifications

Tooling
Software coding
Accessibility testing
Scalable systems
Scalability
UI
Quality check automation
DevOps automation
Technical architecture

Full Job Description

We are seeking a Lead Test Automation Architect to define and lead an enterprise-grade test automation strategy across UI, API, data, and platform layers—while evolving our quality engineering capabilities to support Agentic AI / LLM-based systems (probabilistic outputs, confidence-based validation, hallucination detection, and model evaluation). This role will architect scalable automation frameworks, drive engineering excellence, and accelerate delivery through highly productive, maintainable automation programs.

Key Responsibilities

Automation Strategy & Architecture

Define and own the test automation architecture and multi-layer strategy (UI/API/contract/integration/e2e), aligned to product risk and release cadence.
Establish automation standards, patterns, and reference implementations (framework structure, page-object/screenplay usage, test data patterns, reporting).
Drive shift-left testing practices (contract testing, component testing, pre-merge validation) and reduce reliance on fragile end-to-end tests.

Agentic AI / LLM Test Model Alignment

Design and implement testing approaches for non-deterministic AI outputs, including:
- confidence/threshold-based assertions
- semantic similarity checks and rubric-based validation
- goldens/benchmark datasets and evaluation harnesses
- hallucination and groundedness testing strategies
- regression detection across model/prompt/version changes
Define AI test coverage across accuracy, robustness, bias/safety (as applicable), performance, and reliability.
Partner with ML/AI, Product, and Engineering to establish acceptance criteria for AI capabilities and measurable quality gates.

High-Productivity Automation Programs

Build and scale high-throughput automation projects that are reliable, fast, and easy to extend.
Improve test execution time, reduce flaky tests, and optimize CI/CD pipelines (parallelization, test selection, retries with guardrails).
Create actionable, engineering-friendly quality dashboards (pass/fail trends, flake rate, coverage, defect escape metrics).

Tooling, CI/CD, and Quality Gates

Integrate automation into CI/CD with quality gates (PR checks, nightly suites, release readiness criteria).
Standardize observability for testing: logs, tracing hooks, screenshots/video, artifacts, and test run diagnostics.
Define practices for test environments, data management, service virtualization, and dependency control.

Technical Leadership & Mentorship

Serve as the QE technical authority: coach engineers on automation design, code quality, and best practices.
Lead design reviews for test architecture and automation PRs; enforce coding standards and maintainability.
Influence roadmap and delivery by translating risk into clear testing priorities.

Required Qualifications

8–12+ years in Quality Engineering / Test Automation with architect-level ownership of frameworks and strategy.
Proven experience designing scalable automation across:
- API testing (contract + integration)
- UI automation with stable locator strategies (test IDs, accessibility roles)
- Test data strategy and environment reliability
Strong programming skills in at least one modern language (e.g., Java, Python, TypeScript/JavaScript, C#).
Expertise with automation tools/frameworks (examples):
- UI: Playwright / Cypress / Selenium
- API: REST-assured / SuperTest / Postman/Newman / pytest
- CI: GitHub Actions / Jenkins / Azure DevOps
Demonstrated ability to reduce flakiness and improve execution speed through engineering solutions.

Preferred Qualifications (Strongly Desired)

Experience testing LLM/Agentic AI features or other probabilistic systems, including:
- evaluation harnesses, benchmark datasets, semantic scoring, and drift detection
- prompt/version regression strategies and structured evaluation reports
Familiarity with modern quality approaches: test pyramid, contract testing, consumer-driven contracts, risk-based testing, shift-left.
Knowledge of security/performance testing integration (baseline understanding; hands-on is a plus).
Experience building reusable automation libraries for multiple teams/products.

Our Interview Practices

To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process.

Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process.

Compensation:

$89,600.00 - $157,000.00 USD

This role is eligible for Bonus.

Compensation range listed is based on primary location of the position. Actual base salary offer is influenced by a wide array of factors including but not limited to skills, experience and actual hiring location. Your recruiter can share more information about the specific offer for the job location during the hiring process.

Additional Information:

Wolters Kluwer offers a wide variety of competitive benefits and programs to help meet your needs and balance your work and personal life, including but not limited to: Medical, Dental, & Vision Plans, 401(k), FSA/HSA, Commuter Benefits, Tuition Assistance Plan, Vacation and Sick Time, and Paid Parental Leave. Full details of our benefits are available upon request.

Apply Now

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Key Responsibilities

Automation Strategy & Architecture

Define and own the test automation architecture and multi-layer strategy (UI/API/contract/integration/e2e), aligned to product risk and release cadence.

Establish automation standards, patterns, and reference implementations (framework structure, page-object/screenplay usage, test data patterns, reporting).

Drive shift-left testing practices (contract testing, component testing, pre-merge validation) and reduce reliance on fragile end-to-end tests.

Agentic AI / LLM Test Model Alignment

Design and implement testing approaches for non-deterministic AI outputs, including:

confidence/threshold-based assertions

semantic similarity checks and rubric-based validation

goldens/benchmark datasets and evaluation harnesses

hallucination and groundedness testing strategies

regression detection across model/prompt/version changes

Define AI test coverage across accuracy, robustness, bias/safety (as applicable), performance, and reliability.

Partner with ML/AI, Product, and Engineering to establish acceptance criteria for AI capabilities and measurable quality gates.

High-Productivity Automation Programs

Build and scale high-throughput automation projects that are reliable, fast, and easy to extend.

Improve test execution time, reduce flaky tests, and optimize CI/CD pipelines (parallelization, test selection, retries with guardrails).

Create actionable, engineering-friendly quality dashboards (pass/fail trends, flake rate, coverage, defect escape metrics).

Tooling, CI/CD, and Quality Gates

Integrate automation into CI/CD with quality gates (PR checks, nightly suites, release readiness criteria).

Standardize observability for testing: logs, tracing hooks, screenshots/video, artifacts, and test run diagnostics.

Define practices for test environments, data management, service virtualization, and dependency control.

Technical Leadership & Mentorship

Serve as the QE technical authority: coach engineers on automation design, code quality, and best practices.

Lead design reviews for test architecture and automation PRs; enforce coding standards and maintainability.

Influence roadmap and delivery by translating risk into clear testing priorities.

Required Qualifications

8–12+ years in Quality Engineering / Test Automation with architect-level ownership of frameworks and strategy.

Proven experience designing scalable automation across:

API testing (contract + integration)

UI automation with stable locator strategies (test IDs, accessibility roles)

Test data strategy and environment reliability

Strong programming skills in at least one modern language (e.g., Java, Python, TypeScript/JavaScript, C#).

Expertise with automation tools/frameworks (examples):

UI: Playwright / Cypress / Selenium

API: REST-assured / SuperTest / Postman/Newman / pytest

CI: GitHub Actions / Jenkins / Azure DevOps

Demonstrated ability to reduce flakiness and improve execution speed through engineering solutions.

Preferred Qualifications (Strongly Desired)

Experience testing LLM/Agentic AI features or other probabilistic systems, including:

evaluation harnesses, benchmark datasets, semantic scoring, and drift detection

prompt/version regression strategies and structured evaluation reports

Familiarity with modern quality approaches: test pyramid, contract testing, consumer-driven contracts, risk-based testing, shift-left.

Knowledge of security/performance testing integration (baseline understanding; hands-on is a plus).

Experience building reusable automation libraries for multiple teams/products.

Our Interview Practices

Additional Information: