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,