Agentic DevEx Engineer - New York - Ekho

Description

We're hiring an Agentic DevEx Engineer - a systems-minded engineer who owns the infrastructure, standards, and tooling that make AI agents effective collaborators in our codebase. This is a rare role at the intersection of Platform Engineering, Developer Experience, and AI. You'll be responsible for transforming how humans and agents work together at Ekho. Today, AI coding agents are powerful but inconsistent. They hallucinate file paths, miss context, violate conventions, and require constant hand-holding. Your job is to eliminate that friction.
Singular Mandate: Maximize the effectiveness of agents inside Ekho's codebase so that both humans and AI can execute predictably, safely, and at scale.
Responsibilities
Codebase Architecture & Structure

• Define and enforce architectural boundaries: establish clear domain separation, eliminate duplicated modules, and create discoverable code organization.
• Build reproducible development environments: standardized commands (dev, test, lint, build) that work identically for humans and agents across all surfaces.
• Maintain the single source of truth for how the codebase is organized, documented in files per domain.

Agent Infrastructure & Tooling

• Own AGENTS.MD / CLAUDE.MD: transform our agent instructions from verbose, ad-hoc documents into concise, high-signal guides that improve agent accuracy.
• Design and implement agent hooks: deterministic workflows for how agents plan, read context, execute changes, and validate output.
• Build a library of reusable agent skills: well-defined, domain-specific capabilities (e.g., "analyze payment invoice", "update Firebase function safely") with clear contracts.
• Ensure agents can navigate the codebase without tribal knowledge - predictable paths, explicit boundaries, and machine-readable context.

Standards & Enforcement

• Codify engineering standards: capture Ekho's conventions (formatting, naming, patterns, error handling) explicitly and enforce them through linters.
• Improve the automated review layer: CI checks, static analysis, and AI-powered reviewers that catch misalignment before human review.
• Own validation infrastructure: improve test reliability, reduce flakiness, establish schema guarantees, and ensure CI provides fast, accurate feedback.
• Create the feedback loop: track recurring issues, refine standards, and continuously improve the system.

Developer Experience & Adoption

• Define work modeling standards: templates for tickets and specs that capture context, constraints, and acceptance criteria explicitly - so agents can execute without ambiguity.
• Build canonical workflows: documented patterns for common tasks (bugfix, feature, refactor) that engineers follow with agent assistance.
• Own AI observability: instrument engineering workflows to track PR cycle time, AI-assisted PR percentage, and other signals of agent effectiveness.
• Drive adoption: train the team on agent collaboration patterns, create demos, and ensure the right way is the easy way.

You Must Be AI-Pilled
This role requires someone who is obsessed with the AI tooling landscape - not casually interested, not "keeping an eye on it," but genuinely compelled to try every new agent, workflow, and integration the moment it drops.
You should be:

• Constantly experimenting. You've tried Claude Code, Cursor, Copilot, Windsurf, Aider, and whatever launched last week. You have strong opinions about where each excels and fails. You've probably built custom workflows or prompts that you're unreasonably proud of.
• A forcing function for adoption. You don't just try new tools - you push the team to adopt the ones that work. You're opinionated about workflows that deliver step-function improvements in efficiency, and you're not shy about advocating for change.
• A teacher and evangelist. You share what you learn - in Slack, in demos, in documentation. You get energy from helping others level up. You understand that tooling only matters if people actually use it.
• Impatient with the status quo. You see manual, repetitive engineering work as a problem to be automated, not a fact of life. You're allergic to "that's just how we do it."

If you're not spending personal time exploring what's possible with AI-assisted development, this role probably isn't a good time.
Required Skills & Experience

• 5+ years as a software engineer, with significant experience in infrastructure, platform, or developer experience roles.
• Strong full-stack fundamentals: you can work across frontend (React/TypeScript) and backend , Firebase/GCP) comfortably.
• Track record of improving developer velocity through tooling, automation, or infrastructure - not just shipping features.
• Excellent systems thinking: you can design for maintainability, enforce boundaries, and see second-order effects.
• Strong written communication: you can turn messy codebases into clear documentation and tribal knowledge into explicit standards.
• Obsessively curious about AI tooling. You've used Claude Code, Cursor, Copilot, or similar extensively. You have opinions about what makes agents succeed or fail. You're excited to push the boundary of what's possible.

Preferred Qualifications

• Experience building internal developer platforms or DevEx tooling from scratch.
• Contributions to open-source AI coding tools or significant / work.
• Background in CI/CD systems, automated testing infrastructure, or static analysis tooling.
• Familiarity with prompt engineering and how LLMs consume context.
• Prior experience at a high-growth startup where you wore multiple hats and moved fast.

Why This Role Matters
We believe the companies that figure out human-agent collaboration first will have a massive, compounding advantage. This isn't about replacing engineers - it's about raising the baseline so humans think more clearly, agents act more reliably, and the organization compounds learning instead of losing it.