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Cummins

AI Software Engineer - Senior

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Job Summary

The AI Software Engineer – Senior is responsible for designing, developing, implementing, and supporting high-quality, scalable software solutions with a focus on enterprise Artificial Intelligence (AI) and agentic systems. This role combines software engineering expertise with modern AI technologies to build secure, reliable, and innovative solutions that address complex business challenges.

The incumbent will participate throughout the software development lifecycle, including requirements gathering, solution design, development, testing, deployment, and support. The role requires collaboration with business stakeholders, architects, and cross-functional teams to develop AI-driven products, enterprise integrations, and automation solutions that deliver measurable business value.

Key Responsibilities Software Development & Engineering
  • Design, develop, test, deploy, and maintain high-quality software applications in compliance with coding standards, technical design requirements, and software engineering best practices.
  • Develop software solutions by analyzing business requirements, studying systems flow, data usage, and operational processes.
  • Create technical designs, flowcharts, documentation, and implementation plans to support software development initiatives.
  • Evaluate solution feasibility through requirements analysis, problem definition, architecture assessments, and technical design reviews.
  • Participate in all phases of the Software Development Lifecycle (SDLC), including planning, development, testing, deployment, and operational support.
  • Conduct code reviews, support testing activities, and ensure software quality through established engineering practices.
AI Solution Development
  • Design and implement enterprise AI solutions using agentic workflows, orchestration frameworks, and large language model (LLM) technologies.
  • Develop multi-agent systems leveraging memory, planning, reasoning, tool usage, and MCP-style interactions.
  • Build production-ready AI workflows that integrate enterprise systems, APIs, data platforms, and business applications.
  • Develop reusable prompts, tools, agents, orchestration pipelines, and AI accelerators.
  • Implement Retrieval-Augmented Generation (RAG) architectures, vector databases, memory systems, and prompt engineering techniques.
Enterprise Integration & Automation
  • Integrate AI agents and solutions with enterprise applications such as SAP, SharePoint, ServiceNow, Jira, databases, APIs, and other business systems.
  • Develop secure tool-calling frameworks and data-access mechanisms for AI workflows.
  • Enable AI-powered automation across engineering, manufacturing, operations, safety, and business functions.
  • Design and implement backend services, APIs, orchestration layers, and deployment pipelines.
Solution Architecture & Technical Leadership
  • Collaborate with architects and engineering teams to design scalable, secure, and maintainable AI solutions.
  • Define application boundaries, interfaces, integrations, deployment models, and non-functional requirements (NFRs).
  • Optimize AI and software solutions for performance, reliability, scalability, observability, and cost efficiency.
  • Support cloud-native deployments and platform modernization initiatives on AWS and Azure.
  • Promote built-in quality, engineering excellence, and agile development practices across teams.
Value Realization & Stakeholder Engagement
  • Partner with business stakeholders to identify high-value AI and automation opportunities.
  • Translate business requirements into technical solutions that drive measurable outcomes.
  • Quantify and communicate business value through productivity improvements, cost savings, quality enhancements, and operational efficiencies.
  • Support pilot implementations and transition proof-of-concepts into production-ready solutions.
Innovation & Continuous Improvement
  • Stay current with emerging technologies, AI frameworks, development tools, and industry best practices.
  • Contribute reusable frameworks, templates, standards, and accelerators that improve engineering productivity.
  • Mentor junior engineers and contribute to the development of technical capabilities across teams.
  • Participate in professional development activities and maintain awareness of evolving AI and software engineering trends.ople approach their work while supporting decentralized decision making.
Qualifications Education
  • Bachelor's, Master's, or equivalent degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, Data Science, or a related technical discipline.
  • Equivalent combination of education and relevant professional experience may be considered.
Certifications (Preferred)
  • Cloud certifications (AWS, Azure, or equivalent).
  • AI/ML or Generative AI certifications.
  • Agile, DevOps, or software engineering certifications.
Compliance Requirements
  • This position may require licensing or authorization for compliance with export controls or sanctions regulations.

Required Skills

Technical Skills

  • Strong programming experience in Python and/or Node.js.
  • Hands-on experience with:
    • LangChain
    • LangGraph
    • CrewAI, AutoGen, Semantic Kernel, or similar agentic AI frameworks
    • OpenAI APIs and/or Azure OpenAI services
    • Model Context Protocol (MCP) concepts and integrations
  • Experience developing REST APIs and enterprise integrations.
  • Strong understanding of:
    • Agent orchestration
    • Tool calling and function execution
    • Retrieval-Augmented Generation (RAG)
    • Vector databases
    • Memory systems
    • Prompt engineering
    • LLM application development
  • Experience with cloud platforms, preferably AWS and/or Azure.
  • Familiarity with Docker, GitHub, CI/CD pipelines, DevOps practices, and infrastructure automation.

Preferred Skills

  • Experience implementing enterprise AI governance, security, and compliance frameworks.
  • Knowledge of AI observability, monitoring, evaluation, and performance measurement techniques.
  • Experience deploying and managing AI solutions in enterprise production environments.
  • Exposure to frontend technologies such as React and Next.js.
  • Understanding of data engineering and data platform concepts.

Experience

Required Experience

  • Significant experience in software engineering, application development, and enterprise solution delivery.
  • Experience working within Agile software development environments.
  • Experience taking software products from requirements gathering through production deployment and operational support.
  • Demonstrated experience collaborating with business users, technical teams, and leadership stakeholders.
  • Strong knowledge of software engineering best practices, including:
    • Coding standards
    • Code reviews
    • Source control management
    • Build and release processes
    • Automated testing
    • Continuous integration and continuous deployment (CI/CD)
    • Application support and operations

Preferred Experience

  • Experience developing and deploying enterprise AI, Generative AI, or intelligent automation solutions.
  • Experience building multi-agent systems and AI orchestration platforms.
  • Experience integrating AI solutions with enterprise applications and business workflows.
  • Experience working in manufacturing, industrial, engineering, or large enterprise environments.

Competencies

Technical Competencies

  • Security & Compliance Principles
  • Programming Principles
  • Data Principles
  • Modern Development Practices
  • Solution Design
  • Demonstrating Mastery

Leadership & Professional Competencies

  • Strategic and Innovative Thinking
  • Technical Passion & Drive
  • Driving Effective Outcomes
  • Engaging with Impact
  • Values Differences
  • Ensuring Customer Success