Job Title: AI-First Engineer – Oracle SCM Applications Suite (AI-Fueled SDLC & Modernization)
Location: Onsite: Atlanta, GA or Dallas, TX. The consultant should be based on either of these two sites.
Duration: 6+ months
Job Description
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.
Job Summary
Seeking an AI-First Engineer with deep Oracle SCM 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 an Oracle SCM 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 across Oracle SCM Cloud and/or Oracle EBS applications.
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 Oracle Cloud upgrade impact assessments.
• Apply AI-driven knowledge discovery to reduce analysis and design cycle times.
2. AI-driven code generation and refactoring
• Use GitHub 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 Oracle 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 Oracle Cloud quarterly releases.
• Leverage AI tools for defect triage, root cause analysis, and predictive quality insights.
4. AI-enabled DevOps, 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 Oracle quarterly release readiness and business impact.
• Support AI-driven deployment automation and environment management practices.
5. Experience with GitHub Copilot, Agentic AI frameworks, prompt engineering, RAG patterns, and AI-assisted developer workflows
• GitHub Copilot
• Oracle Code Assist
• ChatGPT
• Claude
• Agentic AI frameworks
• OCI Generative AI
6. Oracle SCM & Technical Expertise
• Strong hands-on experience with Oracle SCM Cloud and/or Oracle EBS applications
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
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