Developer Experience as a Force Multiplier
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. Developer Experience (DevEx) is the discipline of eliminating toil, embedding AI tooling, and building the internal developer platform that makes every engineer 2-4x more productive.
I build the full DevEx stack: internal developer platforms (IDPs) with self-service CI/CD and observability, AI-augmented toolchains that automate routine work, and DORA metric frameworks that measure and prove the velocity gains. The results are measurable: 55% reduction in developer toil, 70% of routine PR reviews AI-assisted, and 4x faster developer onboarding.
In 2026, DevEx is not a nice-to-have - it is the foundation of an AI-native engineering organization.
Schedule DevEx Assessment
What I Deliver
IDP Build-Outs
Internal developer platforms with self-service CI/CD, observability dashboards, secrets management, environment provisioning, and AI agent tooling. Engineers get what they need without filing tickets or waiting for infrastructure teams.
AI Toolchains
Claude Code, GitHub Copilot, Cursor, and agent-assisted PR review pipelines embedded directly into engineering workflows. AI handles the routine; engineers focus on high-value problems that require human judgment.
DORA Metrics & Velocity
Deployment Frequency, Lead Time, MTTR, and Change Failure Rate - tracked, trended, and tied to engineering investments. I implement DORA metric frameworks and use the data to identify the bottlenecks that limit your velocity ceiling.
Internal Developer Platform
The IDP is the foundation of developer experience. I build platforms that give engineers self-service access to everything they need - eliminating the operational friction that slows teams down.
- Self-service CI/CD: Engineers trigger deployments, create environments, and manage pipelines without infrastructure tickets or team dependencies.
- Observability as standard: Logging, metrics, tracing, and alerting built into every service by default - not retrofitted after incidents.
- 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 access to the same tools engineers use.
AI-Augmented Toolchain
The best AI toolchain is one that engineers actually use. I implement production-grade AI tooling that integrates with existing workflows rather than requiring engineers to change how they work.
- Claude Code & Cursor
AI coding assistants configured with codebase context, company-specific rules, and MCP server access - so every engineer has an AI pair programmer that knows your stack.
- Agent-Assisted PR Reviews
Automated PR review agents that check style, security vulnerabilities, test coverage, and dependency issues before a human reviewer sees the diff - 70% of routine reviews handled autonomously.
- AI-Powered Onboarding
Codebase walkthroughs, architecture Q&A agents, and runbook automation that compress new engineer ramp time from weeks to days.
Measuring Toil Reduction
Developer Toil Reduction
Routine tasks - PR triage, test generation, documentation, dependency updates - handled by AI agents operating continuously and consistently across the engineering workflow.
PR Reviews Automated
70% of routine pull request reviews handled by AI agents - style, security, test coverage, and dependency checks - before a human reviewer sees the diff.
Faster Onboarding
AI-assisted context delivery - codebase walkthroughs, architecture Q&A, and runbook automation - reduces new engineer time-to-first-contribution from weeks to days.