Senior Software Engineer

Develops large-scale software and technology solutions in support of Inspire Brands team member-facing platforms. Supports brand needs across Inspire Brands to deliver interactive, innovative, scalable, and secure technology solutions. Leverages modern cloud-native architectures, AI-powered development tools, and enterprise integration patterns to enhance engineering productivity and business outcomes.

Responsibilities:

• Develop and integrate system APIs and enterprise services.
• Document technical details of coding projects through standardized code comments, software architecture documentation, and flow diagrams.
• Ensure corporate compliance, security, and information security standards are upheld throughout the SDLC.
• Contribute to the maintenance, support, and technology roadmap of key team member-facing solutions and platforms.
• Design and establish software patterns to solve complex business and technology challenges.
• Mentor, support, and guide junior developers through code reviews, architecture discussions, best practices, and pattern reviews.
• Participate in Agile Scrum ceremonies and collaborate with cross-functional teams to deliver high-quality solutions.
• Drive engineering excellence through automation, CI/CD, Test-Driven Development (TDD), and DevOps best practices.
• Evaluate and adopt emerging technologies, including AI-assisted development capabilities, to improve delivery speed and software quality.

Education & Experience Qualifications:

• Bachelors Degree in Computer Science or equivalent work experience required.
• Masters Degree in Computer Science or equivalent work experience preferred.
• 4+ years of experience developing and supporting large-scale, highly transactional, and highly available technology platforms.
• Experience working within Agile Scrum teams.
• Experience with restaurant or retail technologies preferred.
• Experience mentoring and coaching junior developers.
• Continuous Integration and Continuous Delivery (CI/CD) automation experience.
• Strong understanding of enterprise architecture principles, including API-led architectures, middleware, and SOA.
• Experience leveraging modern AI development tools and practices to improve engineering productivity is preferred.

Required Knowledge, Skills & Abilities:

Software Engineering:

• Proficient in enterprise development technologies including Java, Server-Side JavaScript, React, and React Native.
• Strong knowledge of distributed services and API technologies including REST.
• Working experience with enterprise frameworks such as Spring Boot and Spring Framework.
• Deep understanding of microservices architecture using technologies including Java, SQL, Kubernetes, and related cloud-native services.
• Strong software design, problem-solving, and troubleshooting skills.
• Experience documenting architecture, technical designs, and system flows.

Cloud & DevOps:

• Proficiency with source control and CI/CD technologies such as Git, GitHub, and Azure DevOps.
• Strong experience with cloud-native architectures, preferably Microsoft Azure.
• Knowledge of containerized application deployments using Docker, Kubernetes, and Azure Kubernetes Service (AKS).
• Experience implementing automated build, deployment, monitoring, and operational practices.

AI & Developer Productivity:

• Hands-on experience using GitHub Copilot for code generation, code reviews, unit test creation, refactoring, documentation generation, and improving developer productivity.
• Understanding of AI-assisted software development practices, including prompt engineering, AI-driven debugging, and code optimization.
• Experience leveraging Generative AI tools to accelerate software design, knowledge discovery, testing, documentation, and development workflows.
• Knowledge of Model Context Protocol (MCP) Servers and their usage for connecting AI assistants with enterprise tools, APIs, databases, source code repositories, development platforms, and engineering workflows.
• Ability to identify and implement practical AI use cases across the Software Development Lifecycle (SDLC), including development, testing, deployment, monitoring, operational support, and incident management.
• Experience integrating AI-powered capabilities into enterprise applications while ensuring security, compliance, governance, and responsible AI standards.
• Familiarity with Large Language Models (LLMs), agentic workflows, AI orchestration, and enterprise AI adoption best practices.
• Experience creating Proof of Concepts (POCs) leveraging AI technologies to improve engineering efficiency, software quality, and team productivity.
• Ability to mentor and guide engineers on effective utilization of GitHub Copilot, AI coding assistants, MCP-based integrations,