Artificial Intelligence Engineer - SCM

New York Technology Partners; Atlanta (GA); Remote; USD 120,000 - 160,000; Full time

Job summary

New York Technology Partners in Atlanta, GA seeks an AI-First Engineer with SCM planning expertise to transform how supply chain applications are designed, developed, tested and deployed through AI-enabled engineering practices.

The ideal candidate will drive AI-powered development and modernization initiatives with an SCM background, focusing on planning and measurable business outcomes such as reduced development cycle times and improved code quality.

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: Analyze business requirements, BRDs, user stories, process flows, and SCM specifications with AI.
• 2. AI-driven code generation and refactoring: Develop SCM customizations, integrations, extensions, reports, and automation using AI-enabled practices.
• 3. AI-assisted testing, test automation, and regression strategies: Build AI-powered testing strategies across functional, integration, regression, performance, and UAT.
• 4. AI-enabled DevOps, code review, documentation generation, and operational support: Integrate AI-assisted code reviews, security analysis, docs, and playbooks into CI/CD.
• 5. Experience with GitHub Copilot, Agentic AI frameworks, prompt engineering, RAG patterns, and AI-assisted developer workflows: Leverage modern AI tooling to accelerate delivery.

Job description

Location: Atlanta, GA – Remote

Duration: W2 Long term contract

Job Summary 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

• 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 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 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

Business Impact & Measurement

Ability to quantify and communicate outcomes from AI adoption, including: