AI Agent Platform Engineer — Multi‑Step LLM Workflows

starlims; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

STARLIMS is seeking an experienced software engineer to advance agentic systems that reason over tasks, call tools, and hand results to humans for approval. You will work on the agent platform, runtime, and production agents, with a focus on reliability, observability, and controlled execution in enterprise environments.

You will design planning loops, tool calls, data access layers, and end-to-end safeguards while grounding decisions in enterprise data and ensuring auditability across releases.

Qualifications

• 6+ years of software engineering experience, including production systems.
• Experience building production LLM systems, including tool-using or multi-step agentic workflows beyond simple prompting and chat interfaces.
• Strong understanding of LLM behavior, limitations, and failure modes, especially how errors compound across a multi-step run.
• Experience with LLM APIs, tool and function calling, and designing planning and execution loops.
• Experience evaluating and debugging non-deterministic systems.

Responsibilities

• Design and build the runtime our agents execute on: planning and execution loops, tool calling, state management, durable execution, and failure recovery.
• Build the layer through which agents reach platform data and external systems safely.
• Design coordination, delegation, and handoff across agents and workflows where needed.
• Make agent behavior versionable, testable, measurable, and regression-safe across releases.
• Build reusable primitives so new agents are configured rather than rebuilt from scratch.
• Take a domain workflow from expert conversation to a working agent: goals, actions, execution flow, failure handling, and success criteria.
• Ground agent decisions and outputs in authoritative enterprise data rather than relying on model knowledge alone.
• Implement human-in-the-loop by design, including approval gates, override capture, uncertainty handling, and clear evidence for agent decisions.
• Close the loop: turn user corrections and overrides into signals that measurably improve the agent.
• Build evaluation harnesses for multi-step behavior, not single-response accuracy: task completion, tool-call correctness, groundedness, trajectory quality, and regression across model, prompt, and tool changes.

Skills

Production LLM systems

LLM tool calling

TypeScript

Python

AWS

Distributed systems

Tools

AWS

Terraform

Job description

STARLIMS is seeking an experienced software engineer to advance agentic systems that reason over tasks, call tools, and hand results to humans for approval. You will work on the agent platform, runtime, and production agents, with a focus on reliability, observability, and controlled execution in enterprise environments.

You will design planning loops, tool calls, data access layers, and end-to-end safeguards while grounding decisions in enterprise data and ensuring auditability across releases.