Integrates across multiple APIs and data sources with secure, reliable contracts (auth, sessions, caching, pagination, error handling, rate limits).
Builds production-ready chat/agentic features: streaming UX, session/history, tool/function calling, lightweight orchestration with retries/fallbacks.
Drives engineering excellence: end-to-end troubleshooting and durable fixes; strong SDLC, automated testing, CI/CD, and cloud/platform collaboration.
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Deep hands-on expertise in React (component architecture, performance optimization, state management, accessibility).
Strong backend development experience with Java + Spring Boot (API design, service patterns, integration).
Experience redesigning or modernizing web products, including migration efforts (e.g., legacy/low-code to modern architecture, including micro-frontends).
Strong understanding of API integration across distributed systems (reliability, latency, observability, security).
Proven experience with testing and SDLC best practices and implementing/operating CI/CD pipelines.
Practical understanding of cloud infrastructure and operational concerns (deployment patterns, monitoring/logging, environment management).
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Preferred qualifications, capabilities, and skills
Experience with design systems, component libraries, and testing strategies (functional & behavioral).
Strong product design thinking: simplifying ambiguous requirements into usable flows; comfort collaborating closely with design and stakeholders.
Experience building conversational/agentic features in production (UX, tool/function calling, orchestration patterns).
Familiarity with containers and orchestration concepts (e.g., Kubernetes), plus modern DevOps practices.
Experience with observability practices (metrics, logs, traces) and operational readiness (runbooks, SLOs).
