Senior Platform Engineer -AI / Platform Lead

Matrix Global•USA,

1d•Hybrid

About The Position

We are looking for a hands-on Senior Platform Engineer / Platform Lead to drive platform engineering, developer experience, and technical ownership across the software development lifecycle—from local development to production. In this role, you will build shared engineering capabilities, establish practical developer workflows, and create reliable, reusable solutions that help product engineering teams build, test, deploy, and operate software faster and more confidently. You will combine deep technical execution with cross-team technical leadership, improving engineering productivity, delivery reliability, and operational excellence. This is an opportunity to shape the platform roadmap, influence engineering standards, and help establish a scalable platform function that evolves with the organization.

Requirements

7+ years of experience in platform engineering, developer experience, engineering productivity, internal tooling, DevOps, or a related software engineering discipline.
Experience building, establishing, or significantly improving a shared engineering platform or platform engineering function.
Strong hands-on engineering skills and the ability to design, build, deploy, and operate the systems and tools you advocate for.
Demonstrated experience developing internal tools, reusable automation, engineering templates, standardized delivery workflows, or developer paved roads adopted by engineering teams.
Strong understanding of CI/CD, release automation, infrastructure, observability, environment management, and production operations within a SaaS environment.
Hands-on experience with AWS is required, including building and operating cloud infrastructure and services in production.
Ability to identify systemic engineering challenges, define practical solutions, and drive technical initiatives across multiple teams.
Strong communication and collaboration skills, with the ability to build trust among engineers, technical leaders, and cross-functional stakeholders.
A pragmatic approach to AI-assisted development, with an emphasis on improving real engineering workflows, quality, and productivity rather than adopting tools for their own sake.

Nice To Haves

Production-grade Go development experience.
Experience modernizing or supporting legacy C#/.NET applications, particularly within Windows-based environments.
Familiarity with TypeScript, React, or frontend engineering platforms.
Experience building internal developer portals, service scaffolding, golden paths, or self-service engineering capabilities.
Experience modernizing legacy systems within a mature, production SaaS environment.
Background in fintech, payments, accounting, eCommerce, property development or management, or other transaction-intensive business domains.
Experience with security controls, identity and access management, compliance-aware delivery, or policy-as-code.

Responsibilities

Drive the platform roadmap: Define and execute a practical roadmap spanning CI/CD, release automation, infrastructure, environments, observability, developer tooling, and engineering productivity.
Build developer paved roads: Develop internal tools, reusable templates, automation, and standardized workflows that simplify common engineering tasks and make best practices easy to adopt.
Improve developer experience: Identify and eliminate friction across local development, testing, deployment, environment management, and production troubleshooting.
Strengthen delivery reliability: Implement automated validation, quality gates, release guardrails, and faster feedback loops to improve software quality and release confidence.
Partner with engineering teams: Work closely with product engineers to understand their challenges and translate recurring delivery bottlenecks into scalable platform improvements.
Apply a platform-as-a-product mindset: Treat engineering teams as internal customers, prioritizing usability, adoption, reliability, and measurable business impact.
Measure engineering effectiveness: Use DORA metrics, developer experience (DX) indicators, and engineering productivity data to identify bottlenecks, guide prioritization, and track improvements.
Enable AI-first engineering: Introduce practical AI-assisted workflows for coding, debugging, testing, code reviews, and documentation using tools such as Claude Code, AI coding agents, MCP-connected workflows, and LLM-based automation.
Raise engineering standards: Provide technical guidance through architecture and design reviews, code reviews, pairing, documentation, and hands-on examples.
Provide technical leadership: Drive cross-functional initiatives, influence engineering decisions, and establish technical direction, with the potential to grow into formal platform leadership as the function matures.