Senior AI-Native Software Engineer Resume Example for AI-Native Web Platform Engineering

Led AI-native software engineering initiatives across multi-agent orchestration, Claude Code workflows, MCP tool architecture, agent skill file design, AI agent code review, vector database systems, and scalable SaaS web platform modernization.

• Architected AI-native web application delivery workflows using Claude Code, Cursor, GitHub Copilot, and multi-agent development patterns, reducing feature implementation cycle time by 40% across SaaS platform initiatives.
• Designed and maintained reusable agent skill files, coding standards, architecture guidelines, and project instructions enabling AI agents to consistently implement features across multiple repositories and engineering workflows.
• Established Model Context Protocol (MCP) tool architecture integrating GitHub, browser automation, filesystem, and custom platform services, improving AI agent access to development workflows and operational context.
• Created feature implementation plans, task decomposition strategies, technical design reviews, and Claude-based agent execution workflows, enabling multiple AI agents to work in parallel on complex product initiatives.
• Reviewed AI-generated code, pull requests, architecture decisions, and implementation plans, improving code quality, reducing rework, and increasing engineering team confidence in AI-assisted delivery.
• Defined AI agent quality gates covering code review standards, testing requirements, architecture compliance, documentation expectations, and production readiness checks before deployment.
• Implemented multi-agent orchestration workflows where specialized planning, coding, testing, and review agents collaborated to deliver full-stack product features with reduced manual coordination overhead.
• Built AI-assisted engineering workflows for requirement analysis, solution comparison, tradeoff evaluation, technical planning, and implementation guidance, accelerating engineering decision-making across product teams.
• Developed MCP-compatible tools and service integrations enabling AI agents to interact with internal platform APIs, deployment workflows, documentation systems, and development environments.
• Led technical design sessions evaluating architecture alternatives, scalability considerations, caching strategies, fallback mechanisms, retry policies, and operational tradeoffs for AI-native web platforms.
• Authored agent skill files defining coding conventions, architectural patterns, repository standards, testing requirements, and feature implementation expectations for AI-assisted software development.
• Served as technical reviewer for AI-generated implementations, validating architecture alignment, business requirements, code quality, security considerations, and maintainability standards.
• Created structured feature plans and task hierarchies for Claude Code and AI agents, enabling parallel execution of development, testing, documentation, and review activities.
• Continuously refined AI agent instructions, prompts, workflows, and evaluation criteria to improve implementation consistency, reduce hallucinations, and increase delivery quality.

Supported modern web engineering, cloud-native development, CI/CD automation, API integration, and early AI-assisted software delivery workflows across scalable SaaS platform environments.

• Developed scalable React, Next.js, TypeScript, and Node.js application workflows supporting enterprise SaaS modernization initiatives and improving frontend delivery consistency.
• Built CI/CD automation and cloud-native deployment workflows using GitHub Actions, Docker, AWS, and infrastructure automation patterns, improving release repeatability across engineering teams.
• Collaborated with engineering teams to modernize distributed systems architecture, observability workflows, API integrations, and server-side rendering patterns across SaaS platform environments.
• Implemented prompt-driven engineering productivity pilots using ChatGPT, GitHub Copilot, and reusable prompt templates to improve code review preparation, test planning, and documentation quality.
• Supported API integration and scalable server-side rendering workflows across REST APIs, GraphQL services, PostgreSQL, Redis, and cloud-native application infrastructure.