Lead Software Engineer

LCS is looking for a Lead Software Engineer to oversee the architecture and development of a new Mobile Backend-for-Frontend platform. This role will provide technical direction and ensure the platform is reliable and maintainable while also leading AI-enabled development practices within the team.

Responsibilities:

• Lead the design, build, deployment, and ongoing maintenance of the Mobile BFF platform that supports the React Native rewrite and front-end ordering experiences across KFC digital channels and 3,400+ restaurants
• Own technical architecture for the BFF service layer, including API boundaries, service responsibilities, data contracts, caching, error handling, security posture, performance, scalability, and production reliability
• Build and guide cloud-native implementation using Node.js, TypeScript, AWS services, infrastructure-as-code, CI/CD pipelines, secrets management, monitoring, logging, and release automation
• Architect the split between the new BFF, the React Native application, and direct Yum integrations, ensuring the app consumes generated TypeScript types from OpenAPI and GraphQL contracts in a clean, maintainable way
• Lead the retirement of selected kfc-sdk responsibilities by moving server-appropriate logic into the BFF while ensuring cart, checkout, loyalty, customer, address, saved payment, and auth flows remain aligned to the approved client or Yum-direct integration patterns
• Port, validate, and harden business-critical menu transformation logic, including menu merge/decorate behavior, Contentful enrichment, daypart resolution, feature flag handling, store-scoped menu responses, and golden-fixture validation
• Define and maintain the BFF API contract strategy, including OpenAPI specifications, endpoint versioning, request/response models, reusable DTOs, schema validation, and compatibility expectations for React Native and future web consumers
• Partner closely with React Native engineers to shape the BFFClient, repository layer, application state flow, caching model, fallback behavior, and front-end architecture that depends on the BFF platform
• Lead the team's AI-enabled development strategy for the BFF and React Native rewrite, including how engineers use AI coding assistants, code agents, prompt patterns, reusable project context, code generation, and review workflows in day-to-day delivery
• Establish responsible AI development standards for secure code generation, human review, traceability, test validation, documentation quality, data protection, and approved tool usage
• Create reusable AI playbooks, prompts, examples, scaffolds, and documentation patterns for API implementation, contract updates, test generation, refactoring, legacy SDK analysis, incident investigation, and troubleshooting
• Coach and mentor engineers in effective AI-assisted development through pairing, demos, working sessions, code reviews, and practical examples that help the broader development team build confidence and capability
• Partner with QA/QE and DevOps to apply AI to unit and integration test generation, golden-fixture coverage, release risk review, log analysis, defect triage, incident summaries, and regression acceleration
• Measure and improve AI adoption using practical engineering outcomes such as developer cycle time, code review quality, test coverage, defect reduction, documentation completeness, and reduced rework
• Establish production-grade deployment practices, including AWS CDK or equivalent IaC, API Gateway/Lambda or approved runtime architecture, CloudFront/caching policies, custom domains, environment configuration, rollback strategy, and release readiness gates
• Implement observability and operational support practices, including Datadog/APM or equivalent tracing, structured logs, dashboards, alarms, runbooks, service health checks, and production incident response routines
• Drive performance and cost optimization across API latency, cache hit rates, upstream Yum and Contentful calls, payload size, cold starts, cloud resource usage, and traffic patterns during peak restaurant ordering windows
• Ensure secure engineering practices, including OIDC/JWKS bearer-token validation, least-privilege cloud permissions, secure secret storage, PII/payment data boundaries, dependency management, and audit-ready operational controls
• Partner with Product, Engineering, QA/QE, DevOps, Security, BYTE, D&T, Yum platform teams, vendors, and front-end teams to align roadmap, dependencies, release timing, and implementation decisions
• Mentor engineers and contractors through design reviews, code reviews, pairing, technical documentation, architecture decision records, and consistent engineering standards
• Represent the BFF, React Native rewrite, and AI-enabled development strategy in cross-functional discussions, clearly communicating technical tradeoffs, risks, dependencies, and recommendations to technical and non-technical stakeholders

Requirements:

• Advanced experience as a lead software engineer, backend/platform engineer, or software architect designing production systems used by mobile and web applications
• Deep expertise in TypeScript, Node.js, REST API design, OpenAPI, GraphQL integration patterns, and contract-driven development
• Strong understanding of BFF architecture, service boundaries, API versioning, DTO design, caching, orchestration, data transformation, and front-end consumption patterns
• Hands-on cloud engineering experience with AWS services such as Lambda, API Gateway, CloudFront, Secrets Manager, CloudWatch, IAM, ACM/Route53, and CDK or comparable infrastructure-as-code tooling
• Experience deploying and maintaining cloud services through CI/CD pipelines, environment management, automated testing, release gates, rollback practices, and production support routines
• Strong understanding of React, React Native, Apollo Client, TypeScript code generation, repository patterns, application state flow, and how front-end architectures consume backend contracts
• Experience with enterprise integrations, preferably including commerce, ordering, menu, pricing, loyalty, CMS, feature flags, store/location, or restaurant technology platforms
• Ability to interpret and port existing SDK or legacy business logic into a cleaner service architecture without creating unnecessary behavior changes or migration risk
• Demonstrated ability to lead AI-assisted software development adoption across engineering teams, including coaching developers, defining guardrails, and improving team productivity
• Practical fluency with modern AI coding tools, code agents, prompt/context engineering, generated test scaffolds, AI-assisted refactoring, documentation generation, and code review support
• Ability to evaluate AI-generated output critically, verify generated code through tests and review, and enforce responsible human-in-the-loop engineering practices for secure enterprise software
• Experience creating team standards or playbooks for AI use in the SDLC, including approved tooling, data handling, prompt hygiene, security boundaries, IP/privacy considerations, and measurable adoption practices
• Strong change-leadership skills to bring skeptical or inexperienced developers along through coaching, pairing, demos, training, and practical wins
• Strong production reliability mindset, including observability, distributed tracing, structured logging, alerting, incident response, runbooks, root-cause analysis, and continuous improvement
• Ability to make technical decisions that balance delivery speed, system reliability, long-term maintainability, security, performance, cloud cost, and responsible AI acceleration
• Comfortable leading through influence across engineers, contractors, vendors, product owners, platform teams, and non-technical stakeholders
• Strong written and verbal communication skills, including architecture diagrams, technical strategy, implementation plans, decision records, AI usage guidance, and executive-level tradeoff explanations
• Familiarity with secure development practices, token-based auth, JWKS validation, secrets management, privacy boundaries, and payment/PII avoidance patterns
• Experience improving engineering quality through automated tests, golden fixtures, contract tests, code review standards, quality gates, AI-supported testing, and production feedback loops