FreeHire

Cummins

AI Software Engineer - Senior

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Job Summary:

The AI Software Engineer – Senior is responsible for designing, developing, and maintaining scalable, secure, and high-performing AI-powered software solutions and platforms. This role focuses on building reusable agentic AI frameworks, enterprise integrations, orchestration capabilities, governance mechanisms, and development accelerators that enable the rapid delivery of enterprise AI solutions.

The position combines strong software engineering principles with modern AI platform development, ensuring solutions are built using industry best practices, enterprise architecture standards, and modern development methodologies. The successful candidate will collaborate with cross-functional teams to deliver innovative, reliable, and compliant AI systems that drive business value while maintaining security, observability, and operational excellence.

Key Responsibilities:

AI Platform & Framework Development
  • Design, develop, and maintain reusable agentic AI frameworks, orchestration templates, and platform accelerators.
  • Build shared services and libraries for agent communication, workflow orchestration, memory management, tool integration, and evaluation pipelines.
  • Establish standardized engineering patterns and best practices for enterprise AI solutions.
  • Develop scalable, secure, and maintainable software solutions throughout the software development lifecycle.
Enterprise Integration & MCP Enablement
  • Develop and maintain MCP-compatible integrations and enterprise connectors.
  • Build reusable APIs, services, and integration frameworks for enterprise platforms and business applications.
  • Enable secure and scalable access to enterprise data sources, systems, and business tools.
  • Design and implement event-driven and cloud-native integration architectures.
Governance, Security & Compliance
  • Define and implement governance standards for enterprise AI systems.
  • Build mechanisms for traceability, auditability, monitoring, lifecycle management, and operational oversight.
  • Support Trust, Risk, Security, and Monitoring (TRiSM) requirements across AI solutions.
  • Ensure adherence to security, privacy, compliance, and regulatory requirements throughout the development lifecycle.
  • Establish observability, evaluation, and monitoring practices to ensure responsible AI deployment.
Software Engineering & Solution Design
  • Analyze business requirements and translate them into scalable technical solutions and architectures.
  • Design application interfaces, define system interactions, and establish non-functional requirements.
  • Evaluate technical feasibility, define solution architectures, and recommend implementation approaches.
  • Develop software following coding standards, testing practices, and quality assurance processes.
  • Participate in code reviews, testing, deployment, and production support activities.
  • Document solutions, technical designs, workflows, and implementation standards.
Engineering Enablement & Operational Excellence
  • Create reusable templates, starter kits, deployment accelerators, and implementation guides.
  • Improve developer productivity through automation, tooling, and standardized development practices.
  • Support production readiness, monitoring, telemetry, guardrails, and fallback strategies.
  • Collaborate with solution teams to improve system reliability, scalability, maintainability, and performance.
  • Contribute to continuous improvement initiatives and adoption of modern engineering practices.
Innovation & Technical Leadership
  • Stay current with emerging technologies, AI frameworks, and industry trends.
  • Recommend innovative approaches, tools, and technologies that improve business outcomes.
  • Support Agile, DevSecOps, and continuous delivery practices.
  • Mentor team members and contribute to the organization's technical excellence and learning culture.
Required Qualifications
  • Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related technical discipline, or equivalent professional experience.
  • Experience developing enterprise software applications using modern software engineering practices.
  • Experience working in Agile development environments.
  • Proven experience delivering solutions through the full software development lifecycle, from requirements gathering through production deployment.
  • Strong understanding of software architecture, design patterns, and scalable system development.

Core Competencies

  • Security & Compliance Principles
  • Programming Principles
  • Data Principles
  • Modern Development Practices
  • Solution Design
  • Strategic & Innovative Thinking
  • Technical Passion & Continuous Learning
  • Driving Effective Outcomes
  • Engaging with Impact
  • Customer Focus
  • Collaboration & Team Agility
  • Valuing Diverse Perspectives
  • Built-In Quality Mindset

Required Skills & Experience

Software Engineering & Architecture

  • Strong software architecture and platform engineering expertise.
  • Experience designing and building reusable platforms, frameworks, and shared services.
  • Strong backend development experience using Python and/or Node.js.
  • Experience with APIs, microservices, event-driven architectures, and cloud-native systems.
  • Deep understanding of software development best practices including:
    • Coding standards
    • Code reviews
    • Source control management
    • Automated testing
    • CI/CD pipelines
    • Production operations

AI & Agentic Systems

  • Experience building enterprise AI and agentic AI solutions.
  • Expertise with one or more of the following frameworks:
    • LangGraph
    • Semantic Kernel
    • AutoGen
    • CrewAI
  • Strong understanding of AI orchestration frameworks and AI system architecture.
  • Experience developing workflow automation, tool orchestration, and multi-agent systems.
  • Familiarity with MCP ecosystem concepts and enterprise AI integration patterns.

Governance, Security & Observability

  • Strong understanding of:
    • AI governance
    • Security and compliance principles
    • Observability and monitoring frameworks
    • Evaluation and testing methodologies
    • Operational risk management
  • Experience implementing monitoring, telemetry, logging, and audit capabilities.
  • Knowledge of enterprise security and compliance requirements.

Modern Development Practices

  • Experience with Agile, DevOps, and DevSecOps methodologies.
  • Understanding of CI/CD pipelines, Infrastructure as Code (IaC), and automated testing frameworks.
  • Ability to apply modern engineering practices that improve delivery speed, quality, and reliability.

Preferred Qualifications

  • Experience with enterprise governance and risk management frameworks.
  • Familiarity with AI risk management, responsible AI, and compliance practices.
  • Experience in platform engineering and developer enablement.
  • Knowledge of vector databases, Retrieval-Augmented Generation (RAG), enterprise search architectures, and knowledge management systems.
  • Experience working in large-scale enterprise environments.
  • Relevant technical certifications in cloud, software engineering, AI, or architecture disciplines.