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All Domain Anomaly Resolution Jobs in Monroe, GA

This is a cross-trained, multi-domain role supporting the organization's move from five separate ... Apply Knowledge Base (KB) articles as the first line of resolution; use AI-assisted tools (Cognigy ...

This is a cross-trained, multi-domain role supporting the organization's move from five separate ... Apply Knowledge Base (KB) articles as the first line of resolution; use AI-assisted tools (Cognigy ...

All Domain Anomaly Resolution information

See Monroe, GA salary details

$15

$25

$42

How much do all domain anomaly resolution jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for all domain anomaly resolution in Monroe, GA is $25.83, according to ZipRecruiter salary data. Most workers in this role earn between $21.35 and $31.59 per hour, depending on experience, location, and employer.

What is all domain anomaly resolution?

All Domain Anomaly Resolution (ADAR) refers to a multidisciplinary approach used by organizations, particularly within the U.S. Department of Defense and intelligence community, to identify, analyze, and resolve unexplained phenomena or anomalies that occur across multiple operational domains such as air, space, land, sea, and cyberspace. The goal is to enhance national security by understanding the nature and origin of these anomalies, which can include unidentified aerial phenomena (UAPs) or unexplainable technological activities. ADAR programs often coordinate between various agencies to gather data, investigate incidents, and recommend policy or procedural changes based on their findings.

What skills and qualifications are needed to thrive as an all domain anomaly resolution analyst?

To thrive as an All Domain Anomaly Resolution analyst, you need expertise in data analysis, intelligence gathering, and cross-domain situational assessment, typically supported by a background in military, intelligence, or related technical fields. Familiarity with analytical software, classified information management systems, and intelligence reporting platforms is essential. Strong critical thinking, attention to detail, and effective communication skills enable analysts to interpret ambiguous data and collaborate across agencies. These competencies are crucial for accurately identifying, investigating, and resolving potential threats or anomalies across multiple operational domains.

What are the typical challenges faced by professionals working in all domain anomaly resolution, and how can they be addressed?

Professionals in All Domain Anomaly Resolution often encounter challenges such as rapidly evolving threats, large volumes of complex data from multiple sources, and the need for timely, coordinated responses across different domains (air, space, cyber, etc.). Success in this role often depends on strong analytical skills, the ability to work collaboratively with multidisciplinary teams, and continuous learning to stay current on emerging technologies and tactics. Developing effective communication channels and leveraging advanced data analysis tools can help address these challenges and improve response times.

What is the difference between All Domain Anomaly Resolution vs Network Security Analyst?

AspectAll Domain Anomaly ResolutionNetwork Security Analyst
Required CredentialsCertifications like CISSP, CEH, or network-specific trainingCertifications such as CompTIA Security+, CISSP, or Cisco CCNA Security
Work EnvironmentFocuses on monitoring and resolving anomalies across multiple domains and systemsPrimarily monitors and protects network infrastructure and security
Employer & Industry UsageUsed in cybersecurity, IT operations, and incident response teamsCommon in cybersecurity, IT departments, and enterprise networks

All Domain Anomaly Resolution specialists focus on identifying and resolving irregularities across various digital domains, ensuring system integrity. Network Security Analysts primarily protect network infrastructure from threats. While both roles involve cybersecurity skills and certifications, All Domain Anomaly Resolution has a broader scope across multiple systems, whereas Network Security Analysts concentrate on network-specific security measures.

What job categories do people searching All Domain Anomaly Resolution jobs in Monroe, GA look for?

The top searched job categories for All Domain Anomaly Resolution jobs in Monroe, GA are:

What cities near Monroe, GA are hiring for All Domain Anomaly Resolution jobs?

Cities near Monroe, GA with the most All Domain Anomaly Resolution job openings:

Senior Quality Engineer, Data & AI Platform

M3

Lawrenceville, GA • On-site

$81K - $110K/yr

Full-time

Re-posted 12 days ago


Key responsibilities

  • Design and execute test strategies for AI/ML-enabled features, including defining acceptance thresholds.

  • Validate AI-generated outputs against domain-grounded acceptance criteria and build regression suites to detect drift and degradation.

  • Review application logs, monitor model performance, and coordinate testing activities across releases and system updates.


Job description

M3 (www.m3as.com) is a leading provider of hospitality-specific software solutions, delivering cloud-based tools for hotel accounting, financial reporting, labor management, payroll, and business intelligence. Built by hoteliers for hoteliers, M3 empowers hotel owners, operators, and management companies to streamline back-office operations, reduce costs, gain real-time insights, and drive portfolio performance across thousands of properties in North America and beyond.

Description Summary:        

The Senior Quality Engineer - Data & AI sits at the intersection of data engineering, machine learning product delivery, and quality assurance. This role is responsible for validating AI-powered and data-driven features across M3's hospitality accounting platform, with primary ownership of quality for the Data & AI team's active products.

Unlike traditional QE roles focused on deterministic software, this position requires quality thinking applied to systems that produce probabilistic outputs - where "passing" is defined by accuracy thresholds, not binary correctness. The right candidate understands how AI products fail differently and is motivated to build the testing practice that prevents those failures from reaching customers.

This is an individual contributor role with significant cross-functional responsibility, including collaboration with Engineering, Product, Data, and external contractor teams.

Essential Duties:

The duties listed below are the essential functions of this position, and they may change as the needs of the company demand. All associates are expected to do what is necessary to get the work done and to cooperate fully with their supervisor's requests for additional or altered duties. 

  • Design and execute test strategies for AI/ML-enabled features; define acceptance thresholds (precision, recall, F1) in partnership with Engineering and Product.
  • Validate AI-generated outputs - mappings, anomaly flags, LLM summaries - against domain-grounded acceptance criteria; build regression suites to detect concept drift, label drift, and feature degradation.
  • Evaluate LLM outputs for accuracy, hallucination, and format compliance; build prompt regression test suites to catch behavior changes when model versions or system prompts are updated.
  • Write data validation logic in Python or SQL against tables across Bronze, Silver, and Gold layers; design and automate data contract tests covering schema, null rates, referential integrity, and row counts.
  • Implement automated validation checkpoints in Databricks pipelines; apply statistical testing methods to pipeline output validation.
  • Define and instrument production monitoring checks for model performance: alert thresholds, confidence degradation, and data drift.
  • Perform functional, regression, integration, and exploratory testing across releases, enhancements, and defect fixes using manual and automated approaches.
  • Design, develop, maintain, and execute automated test scripts using frameworks such as Katalon and Playwright; support CI/CD quality gates.
  • Develop and execute test cases for ETL processes, data warehouse workflows, and API validation.
  • Review application logs and monitoring tools to identify and track defects; coordinate builds, deployments, and software migrations for planned releases and hotfixes.
  • Maintain and utilize testing tools including Azure DevOps, SQL Server, Postman/Swagger/SoapUI, and automation frameworks.
  • Participate in Agile ceremonies; collaborate with Product, Engineering, Data, and external contractor teams to ensure comprehensive coverage and aligned acceptance criteria.
  • Communicate quality risks early and in writing; develop and maintain a QE runbook for AI/ML products.
  • Adhere to secure testing practices and compliance requirements associated with the assigned Technical Security Level.
  • Other duties as assigned.

Education/Training/Experience:

  • Minimum of 5 years of experience in software testing or quality engineering, with at least 2 years focused on data-intensive or AI/ML-enabled products.
  • Bachelor's Degree in Computer Science, IT, MIS, or equivalent combination of education and experience.
  • 3+ years of hands-on experience with Microsoft SQL Server for data validation and testing; experience using Azure DevOps for test management and release coordination.
  • 2+ years of API testing experience (Postman, Swagger, or SoapUI); experience writing and executing test cases for ETL processes and data warehouse environments.
  • Hands-on experience with Databricks, Delta Lake, or equivalent Lakehouse platforms and Python for data validation strongly preferred.
  • Experience validating ML model outputs and familiarity with LLM evaluation, prompt regression testing, or generative AI quality workflows preferred.
  • Familiarity with dbt, Great Expectations, MLflow, or equivalent pipeline-level assertion and experiment tracking tools preferred.
  • Working experience with automation frameworks such as Katalon or Playwright; familiarity with USALI and hospitality GL structure a plus.
  • Understanding of Agile/Scrum methodologies; strong written and verbal communication skills.
  • CSQA or ISTQB certification strongly preferred; certifications in AI/ML quality or data engineering are welcomed.