AI Platform Engineer

Aalo Atomics•Austin, TX

1d•Onsite

About The Position

We’re hiring an AI Platform Engineer to maintain, evolve, and continuously improve the horizontal systems that make AI usable, secure, and scalable across the company. At Aalo, 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 AI team maintaining and evolving an existing internal platform that powers AI across the company. Your job is to maintain, 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

Requirements

Strong engineering fundamentals
Design platform systems with clear APIs, service boundaries, data models, and integration patterns
Write clear, maintainable code and have strong judgment around reliability, security, and operational quality
Know when to move carefully on shared or mission-critical systems and when to avoid unnecessary complexity
High output and follow-through
Comfortable taking ambiguous platform needs and turning them into maintainable systems
Handle integrations, operational issues, and platform hardening without losing momentum
Care about building durable systems that enable many downstream solutions
AI-native working style
Already use AI coding agents or agentic development workflows as part of your daily engineering process
Demonstrated interest in AI through real projects, experiments, evaluations, or sustained use of new models and tools
Can evaluate and refine AI-generated code and reason about how harnesses, tools, and workflows should be structured
Think in terms of leverage, evaluation loops, and multiplier effects across the platform
Team-first mindset
Comfortable building shared systems and enabling other engineers through reusable patterns and capabilities
Collaborate closely and communicate clearly across engineering, security, and operational contexts
Optimize for platform quality, long-term maintainability, and collective progress
Interest in where this is going
Excited by the idea that software engineering is shifting toward higher-level system design, supervision, and evaluation
Want to build the infrastructure that makes AI workflows safe, scalable, and reusable across the company
Comfortable working in a role that will evolve as the system becomes more capable
Based in the United States
Willing to work on-site in Austin, TX

Nice To Haves

Experience with internal developer platforms, API gateways, control planes, or multi-tenant systems
Experience working in regulated, high-reliability, or compliance-sensitive environments
Background in cloud infrastructure, DevOps, security, controls, or operational software
Exposure to AI-driven workflows, evaluation systems, or agent-based platforms

Responsibilities

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