AI-Native Developer Experience
From under 10% test coverage to 40% with no QA team. From three-month onboarding to time-to-first-commit on day one.
The fastest way to accelerate an engineering organization is not to hire more engineers - it is to remove the friction that makes existing engineers slow. AI-Native DevEx is the discipline of operationalizing agentic AI as production infrastructure across the SDLC, building the internal developer platform (IDP) that supports it, and tracking the velocity metrics that prove it.
I run a multi-model strategy across Claude Code, GitHub Copilot, AWS Kiro, OpenAI Codex, Gemini, Bolt, Lovable, and Snowflake Cortex - all wired as first-class GitHub Actions pipeline stages for code generation, test synthesis, cloud architecture scaffolding, and documentation refresh. Multi-model fluency tracked as a leading indicator alongside delivery and quality metrics, so teams develop proficiency across providers with no single-vendor dependency. I have partnered with CPOs on Lovable and jointly with product teams on Bolt to push AI tooling beyond engineering into rapid product prototyping.
Measurable outcomes: 5x deploy frequency, 23% PR throughput gain, code-to-release cycle time down 40%, MTTR cut 30%, and new-engineer onboarding cut 70%.
Hire Me
What I Deliver
AI-Augmented Pipelines
Claude Code, GitHub Copilot, and AWS Kiro running as first-class GitHub Actions stages - code generation, test synthesis, cloud architecture scaffolding, and documentation refresh embedded in every CI/CD run.
Ship Fast, Ship Quality
Hitting a deadline with software that does not work is not shipping - it is creating a cleanup project for next quarter. AI-generated unit tests, Playwright UI regression suites, AI-assisted PR review, and SonarQube quality gates in CI/CD lifted coverage from under 10% to 40% with no dedicated QA team. Speed and quality, not one or the other.
Onboarding That Ships Day One
AI-assisted documentation refresh plus a lead-mentor program cut new-engineer onboarding 70% - measurable DevEx and time-to-first-commit improvement on every new hire.
Velocity Metrics That Matter
MTTR, deploy frequency, PR throughput, SLA attainment, onboarding time-to-productivity, and AI-assisted deployment rate - tracked, trended, and tied to engineering investments. Board-ready, not vague AI-adoption slides.
Multi-Model Fluency
No single-vendor dependency. Teams build proficiency across Claude, GitHub Copilot, AWS Kiro, OpenAI Codex, Gemini, Bolt, Lovable, and Snowflake Cortex. Prompt throughput tracked as a leading indicator alongside delivery metrics.
IDP Build-Outs
Internal developer platforms with self-service CI/CD, CloudWatch Canaries, PagerDuty, custom Grafana dashboards, secrets management, and MCP servers giving AI agents governed access to the same tools engineers use.
Internal Developer Platform
The IDP is the foundation of AI-native DevEx. I build platforms that give engineers self-service access to everything they need - eliminating the operational friction that slows teams down and shifting the org from reactive firefighting to proactive observability.
- Self-service CI/CD with AI stages: Engineers trigger deployments and create environments without infrastructure tickets. Claude Code, GitHub Copilot, and Kiro run as first-class pipeline stages on every build.
- Observability as standard: CloudWatch Canaries, PagerDuty, Datadog, Splunk, and custom Grafana dashboards built into every service by default - executive-level visibility into uptime and platform health for the first time.
- Secrets management: Vault-backed secrets accessible to services and AI agents through governed access controls.
- Agent tooling integration: MCP servers embedded in the IDP so AI agents have governed, auditable access to the same tools engineers use.
Multi-Model AI-Augmented Toolchain
The best AI toolchain is one that engineers actually use. I implement production-grade tooling that integrates with existing workflows rather than requiring engineers to change how they work - and I run it as a multi-model strategy so the org develops fluency across providers.
- Claude Code, GitHub Copilot & AWS Kiro
Frontier coding agents configured with codebase context, company-specific rules, and MCP server access - so every engineer has an AI pair programmer that knows the stack. Early-adopter design partner for AWS Kiro (Amazon Q replacement).
- OpenAI Codex, Gemini, Bolt, Lovable & Snowflake Cortex
OpenAI Codex for prototyping and code review, Gemini for long-context reasoning, Bolt and Lovable for rapid app scaffolding alongside CPOs and product teams, Snowflake Cortex for warehouse-native analytics. Multi-model fluency, no single-vendor dependency.
- Agent-Assisted PR Reviews, Unit Tests & Playwright UI Suites
Automated PR review agents check style, security vulnerabilities, test coverage, and dependency issues before a human reviewer sees the diff. AI-generated unit tests plus Playwright-driven UI regression suites lifted coverage from under 10% to 40% with no dedicated QA team. Speed and quality, not one or the other.
- AI-Powered Onboarding & Documentation
Codebase walkthroughs, architecture Q&A agents, runbook automation, and AI-refreshed documentation that compress new-engineer ramp time 70% - measurable time-to-first-commit on every new hire.
Velocity Outcomes
Real results from operationalizing AI-native DevEx across growth-stage engineering organizations. No vanity metrics, no AI theater.
Deploy Frequency
AI-native SDLC running Claude Code, OpenAI Codex, GitHub Copilot, and AWS Kiro as first-class GitHub Actions stages. Code-to-release cycle time down 40%.
PR Throughput Gain
AI-assisted code review, test generation, and documentation refresh shipped throughput improvements without sacrificing quality. SonarQube enforces project standards in CI/CD.
Onboarding Time Cut
AI-assisted documentation refresh plus a lead-mentor program. Test coverage lifted from under 10% to 40% with no dedicated QA team.