AI First Engineer – SCM Planning

Cognizant; Dallas (TX); On-site; USD 120,000 - 150,000; Full time

Job summary

Cognizant in Dallas seeks an AI First Engineer with deep SCM expertise to transform supply chain software across the SDLC using AI-enabled practices.

The role focuses on AI-driven design, coding, testing, deployment, and operations, delivering measurable improvements in cycle time, code quality, and reliability. Candidates should demonstrate a track record applying AI to planning, development, and operations and be proficient with Copilot, Agentic AI frameworks, and related tools.

Qualifications

• Experience applying AI across the SDLC to supply chain software.
• Strong SCM background with ability to drive AI-enabled modernization.
• Proven track record of measurable business outcomes.

Responsibilities

• Leverage AI to analyze requirements, design, and impact using AI-assisted workflows.
• Drive AI-driven code generation, refactoring, and modernization for SCM applications.
• Develop AI-powered testing, automation, and regression strategies.
• Integrate AI-assisted DevOps, documentation, and runbooks into pipelines.
• Demonstrate and quantify outcomes from AI adoption in delivery and operations.

Skills

AI/ML skills

SCM expertise

CI/CD experience

Tools

GitHub Copilot

Agentic AI frameworks

OCI Generative AI

Prompt engineering

Job description

Job Summary

We are seeking an AI First Engineer with deep 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

Responsibilities

We are seeking an AIFirst Engineer with deep 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 AIenabled 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 AIpowered 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

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Key Responsibilities

• 1 AIAssisted 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 AIassisted workflows

  Utilize AI to accelerate fitgap analysis and quarterly upgrade impact assessments

  Apply AIdriven knowledge discovery to reduce analysis and design cycle times

• 2 AIdriven code generation and refactoring

  Use GitHub Copilot Agentic AI frameworks and similar tools to accelerate development across PLSQL Java JavaScript Groovy VBCS OIC and APEX

  Implement AIassisted code generation code optimization refactoring reverse engineering and legacy modernization initiatives

  Develop SCM customizations integrations extensions reports and automation solutions using AIenabled development practices

  Apply AI to automate integration mappings API creation report generation and technical documentation

• 3 AIassisted testing test automation and regression strategies

  Build AIpowered 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 AIenhanced regression testing frameworks

  Leverage AI tools for defect triage root cause analysis and predictive quality insights

• 4 AIenabled DevOps code review documentation generation and operational support

  Integrate AIassisted code reviews security analysis static code scanning and quality gates into CICD pipelines

  Utilize AI to generate deployment documentation release notes implementation guides and operational runbooks

  Support AIdriven deployment automation and environment management practices

• 5 Experience with GitHub Copilot Agentic AI frameworks prompt engineering RAG patterns and AIassisted developer workflows

  GitHub Copilot

  ChatGPT

  Claude

  Agentic AI frameworks

  OCI Generative AI

Business Impact Measurement

Ability to quantify and communicate outcomes from AI adoption including

• Development cycle time reduction
• Improved code quality
• Reduced defect rates
• Reduced testing effort
• Faster deployment cycles
• Faster incident resolution
• Increased developer productivity