About the role

We’rehiring an AI Platform Engineer tomaintain, evolve, and continuously improve the horizontal systems that make AI usable, secure, and scalable across the company.

AtAalo, AI is becoming part of the operating system of the company. This role focuses on the horizontal foundation: shared gateways, orchestration, observability, security boundaries, deployment patterns, and platform architecture that let many AI solutions be built safely on top.

This role sits at the center of every AI system we deploy.

You will work as part of a small, highly collaborative AIteammaintainingand evolving an existing internal platform that powers AI across the company.

Your job is tomaintain, harden, and evolve the shared substrate that other engineers and agents build on top of.

This is a software and platform engineering role with strong architecture, security, controls, integration, infrastructure, and operational governance responsibilities.

Examples of the platform systems and capabilities this role may touch include:

• AI model gateways, routing, token management, model lifecycle administration, and cost controls

• Agent harnesses, shared skills, tool orchestration, and execution runtimes

• Observability, evaluation, feedback, and audit trails for AI-generated outputs

• Identity, access control, data boundaries, and secure enterprise authentication

• Shared integration layers across manufacturing, engineering, finance, HR,logistics, and document systems

• Deployment infrastructure, CI/CD, and infrastructure as code for AI services

• Internal APIs, reusable services, admin tooling, and operational control planes for AI systems

• Workflow orchestration, browser automation, and background job execution systems

• Platform patterns for regulated, high-reliability, and mission-critical AI applications

Common technology categories and platform patterns in the stack include:

• Backend application development

• Internal web applications and admin interfaces

• Relational and document-oriented databases

• APIs and service-oriented backend systems

• Frontend and internal admin web application frameworks

• Containers, CI/CD, and infrastructure as code

• Cloud platforms, secure storage, identity, and secrets management

• Model providers, gateways, access control, and policy enforcement

• AI coding agents, agent harnesses, orchestration frameworks, and shared tooling

• Observability, evaluation, telemetry, security controls, and feedback systems

What you'll do

• Maintain, evolve, and continuously improve platform architecture, service boundaries, and reusable primitives for AI systems

• Build and harden gateways, orchestration layers, shared tools, and integration frameworks

• Implement deployment patterns, CI/CD workflows, and infrastructure best practices that keep AI systems reliable at scale

• Review, debug, and refine AI-generated code in mission-critical and shared systems

• Build observability, evaluation, feedback, and usage telemetry systems that let AI solutions be measured, audited, and continuously improved

• Establish security boundaries, data access patterns, controls, policy guardrails, and auditability for internal AI systems

• Work closely with solutions engineers and other stakeholders to enable reusable platform capabilities that compound in value over time

Qualifications

Strong engineering fundamentals

• You can design platform systems with clear APIs, service boundaries, data models, and integration patterns

• You write clear, maintainable code and have strong judgment around reliability, security, and operational quality

• You know when to move carefully on shared or mission-critical systems and when to avoid unnecessary complexity

High output and follow-through

• You are comfortable taking ambiguous platform needs and turning them into maintainable systems

• You handle integrations, operational issues, and platform hardening without losing momentum

• You care about building durable systems that enable many downstream solutions

AI-native working style

• You already use AI coding agents or agentic development workflows as part of your daily engineering process

• You havedemonstratedinterest in AI through real projects, experiments, evaluations, or sustained use of new models and tools

• You can evaluate and refine AI-generated code and reason about how harnesses, tools, and workflows should be structured

• You think in terms of leverage, evaluation loops, and multiplier effects across the platform

Team-first mindset

• You are comfortable building shared systems and enabling other engineers through reusable patterns and capabilities

• You collaborate closely and communicate clearly across engineering, security, and operational contexts

• Youoptimizefor platform quality, long-term maintainability, and collective progress

Interest in where this is going

• You are excited by the idea that software engineering is shifting toward higher-level system design, supervision, and evaluation