APP Engineer
Job in
Atlanta, Fulton County, Georgia, 31199, USA
Listed on 2026-10-30
Listing for:
Spectraforce Technologies
Full Time
position Listed on 2026-10-30
Job specializations:
-
Software Development
AI Engineer (Applied/Software), AI QA / Validation Engineer
Job Description & How to Apply Below
APP Engineer
Location:
Atlanta, GA based candidate preferred, Remote also allowed.
Job Description:
AI-First Engineer – SCM- AI-Fueled SDLC & Modernization
AI-first engineer who leverages AI across the entire software development lifecycle -- AI throughout the full software development lifecycle, from requirements through deployment and support.
We are seeking an AI-First Engineer with SCM planning expertise and a proven track record of applying AI across the entire Software Development Lifecycle (SDLC). This role is focused on transforming how Supply Chain applications are designed, developed, tested, deployed, and supported through AI-enabled engineering practices.
The ideal candidate is not simply a developer who occasionally uses AI tools, but an engineer who consistently leverages AI technologies to improve productivity, code quality, testing efficiency, operational support, and overall software delivery outcomes.
The role will drive AI-powered development and modernization initiatives with an SCM background particularly in planning.
Candidates should be able to demonstrate and quantify measurable business outcomes achieved through AI adoption, including reduced development cycle times, improved code quality, lower testing effort, accelerated deployments, and faster issue resolution.
Key Responsibilities
AI-Fueled Software Engineering Across the SDLC
Apply AI-enabled engineering practices throughout all phases of the software lifecycle:
1. AI-Assisted Requirements & Solution Design Leverage AI and LLM technologies to analyze business requirements, BRDs, user stories, process flows, and SCM functional specifications.
Generate technical designs, impact assessments, traceability matrices, solution alternatives, and effort estimates using AI-assisted workflows.
Utilize AI to accelerate fit-gap analysis and quarterly Cloud upgrade impact assessments.
Apply AI-driven knowledge discovery to reduce analysis and design cycle times.
2. AI-driven code generation and refactoring
Use Git Hub Copilot, Oracle Code Assist, Agentic AI frameworks, and similar tools to accelerate development across PL/SQL, Java, JavaScript, Groovy, VBCS, OIC, and APEX.Implement AI-assisted code generation, code optimization, refactoring, reverse engineering, and legacy modernization initiatives.
Develop SCM customizations, integrations, extensions, reports, and automation solutions using AI-enabled development practices.
Apply AI to automate integration mappings, API creation, report generation, and technical documentation.
3. AI-assisted testing, test automation, and regression strategies
Build AI-powered testing strategies covering functional, integration, regression, performance, and user acceptance testing.
Utilize AI for automated test case generation, test data creation, defect identification, and test maintenance.
Implement AI-enhanced regression testing frameworks to support Cloud quarterly releases.
Leverage AI tools for defect triage, root cause analysis, and predictive quality insights.
4. AI-enabled Dev Ops, code review, documentation generation, and operational support
Integrate AI-assisted code reviews, security analysis, static code scanning, and quality gates into CI/CD pipelines.
Utilize AI to generate deployment documentation, release notes, implementation guides, and operational runbooks.
Apply AI-powered monitoring and analytics to assess quarterly release readiness and business impact.
Support AI-driven deployment automation and environment management practices.
5.
Experience with Git Hub Copilot, Agentic AI frameworks, prompt engineering, RAG patterns, and AI-assisted developer workflows
GitHub Copilot Oracle Code Assist
ChatGPTClaude
Agentic AI frameworksOCI Generative AIWindsurf
Business Impact & Measurement
Ability to quantify and communicate outcomes from AI adoption, including:
o Development cycle time reduction
o Improved code quality
o Reduced defect rates
o Reduced testing effort
o Faster deployment cycles
o Faster incident resolution
o Increased developer productivity Applicant Notices & Disclaimers For information on benefits, equal opportunity employment, and location-specific applicant notices,
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