AI Platform Engineer

Job description

Job responsibilities

• Design agentic systems, not prompts
• Author agent definitions and context as engineered artifacts, with explicit role separation, tool allowlists, model selection, and hard behavioral limits.
• Design multi-agent architectures where roles are adversarial and reliable by construction.
• Build skills with disciplined triggering, progressive disclosure, and non-overlapping ownership boundaries - so capability scales without the context window collapsing.
• Orchestrate work across multiple model vendors, harnesses, and agent runtimes.
• Make and defend the call on whether a problem warrants an agent at all. Know when a deterministic script or webhook is the correct answer, and how to engineer away from an agent left polling in a loop.
• Make agents part of the engineering infrastructure
• Design how agents get invoked across the full range of trigger patterns — a developer working interactively at a terminal, a nightly scheduled run, an event-driven trigger like CI, and long-running unattended work.

Job requirements

• 3+ years building software systems, with meaningful recent depth in LLM-based or agentic systems
• Demonstrated fluency with modern agent architecture: tool/function calling, MCP or an equivalent tool protocol, context management, retrieval, multi-agent orchestration, and hooks or middleware for policy enforcement
• Practical judgment about model selection and cost/latency/quality tradeoffs, and a habit of tracking the frontier rather than relying on last year's assumptions
• Experience evaluating AI system quality with rigor
• Experience with analysis of unstructured data
• Experience running non-interactive workloads in production: CI pipelines, scheduled jobs, or event-driven triggers
• Strong Python intuition plus comfort in shell, CI, and Linux service operation
• Experience building and maintaining production applications