AI Engineer, Agent Infrastructure

Zed Financial PH, Inc

$120K — $160K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5-7 years of experience with production AI agents, focusing on execution, evaluation, and reliability.
  • Solid foundation in backend or infrastructure engineering, including distributed systems and APIs.
  • Hands-on experience with workflow orchestration or automation systems.
  • Familiarity with observability and evaluation techniques for AI systems.
  • Ability to connect infrastructure needs with product functionality.

Responsibilities

  • Build and maintain the execution layer for AI agents including task coordination and state management.
  • Define interactions between agents and internal/external systems.
  • Design secure environments for agent operations with permission models.
  • Develop monitoring and debugging tools for agent performance in production.
  • Integrate AI agents seamlessly into existing product workflows with a focus on reliability.
  • Optimize system performance, balancing latency, cost, and quality.

Benefits

  • Equity in the company for long-term investment.
  • Opportunity to work on critical infrastructure for AI deployment.
  • Experience in a fast-moving organization focused on AI advancement.
  • Involvement in impactful projects that influence core business operations.
Full Job Description
The Role

We're hiring an engineer to own the infrastructure layer behind our production AI agents.

This is not a prompt engineering role. It's not a UI role. It's about the harness around LLMs - the systems that determine how agents actually execute tasks, interact with tools, access internal and external systems, stay within permission boundaries, and behave reliably in production.

You'll sit at the intersection of backend infrastructure and product, and what you build will define how AI is deployed across the company.
What You'll Work On
  • Build and own the execution layer for AI agents - task orchestration, tool calling, state management
  • Define how agents interact with internal systems and external APIs
  • Design sandboxed environments and permissioning models for safe, controlled agent execution
  • Build evaluation, monitoring, and debugging infrastructure for agent behavior in production
  • Integrate agents into real product workflows where correctness and reliability are non-negotiable
  • Improve system performance across latency, cost, and quality tradeoffs
What You Bring
  • Direct experience shipping production LLM or agent systems end-to-end - orchestration, evaluation, reliability, not just prototypes
  • Strong backend or infrastructure engineering foundation (distributed systems, APIs, platform engineering)
  • Experience with workflow orchestration, automation systems, or agent frameworks
  • Familiarity with evaluation and observability loops for AI systems
  • Ability to think across both infrastructure concerns and product behavior - this role requires both
Strong Signals
  • You've built agent systems that take real actions, not just generate text
  • You've designed execution environments - task runners, sandboxes, job systems
  • You've worked on AI that's deeply embedded in a real product, not a side project or internal tool
  • You have experience with observability and evaluation loops for AI systems in production
Why This Role

Most teams are still prototyping. We're past that.

This role determines whether our agents are reliable or brittle, safe or risky, useful or demo-only. You'll be building the layer that makes production AI actually work - at a company where AI is core to how we underwrite, operate, and scale.

We hire exceptional people from diverse backgrounds because different perspectives build better products.

If you're excited about this role but don't check every box, apply anyway. We value potential, ownership, and alignment with our values more than perfect re9sume9s.

Compensation includes salary, equity, and benefits. Final offers are based on role scope, location, and experience.

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