AI Agent Platform Engineer — Production-Ready Agents

STARLIMS Corporation; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

STARLIMS Corporation is seeking an experienced software engineer to advance our agent platform and runtime for enterprise-grade LLM-driven workflows.

You will design and build the execution, tool-calling, and governance layers that let autonomous agents operate reliably in regulated environments, with human-in-the-loop oversight and rigorous observability.

Join a team delivering scalable AWS-based infrastructure, durable execution, and end-to-end ownership from idea to production.

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.
• Solid backend and cloud experience (AWS or equivalent).
• Proficiency in TypeScript and/or Python.

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.
• Define production metrics for agent quality, reliability, latency, cost, and human intervention rates.
• Implement guardrails, fallbacks, timeouts, cost ceilings, and end-to-end observability and tracing across agent runs.
• Design safeguards against prompt injection, unsafe tool use, excessive permissions, data leakage, and other agent-specific security risks.
• Manage prompt evolution, model drift, and non-determinism while maintaining consistent, measurable system behavior across releases.
• Integrate agents with platform APIs and third-party enterprise systems already running in our customers’ environments.
• Build retrieval and context pipelines that turn fragmented enterprise data into reliable, permission-aware agent context.
• Design controlled execution paths for automated actions, with a complete, traceable audit trail.
• Build and operate backend services on AWS (Lambda, API Gateway, DynamoDB, Step Functions, etc.)
• Own significant parts of the system architecture and contribute to key technical decisions
• Contribute to infrastructure-as-code and deployment pipelines

Skills

LLM systems design

Backend/cloud engineering

TypeScript/Python

Distributed systems debugging

Performance/cost tradeoffs

End-to-end ownership

Tools

OpenAI API

Anthropic API

Vector search

AWS Lambda

DynamoDB

Step Functions

Terraform

Job description

STARLIMS Corporation is seeking an experienced software engineer to advance our agent platform and runtime for enterprise-grade LLM-driven workflows.

You will design and build the execution, tool-calling, and governance layers that let autonomous agents operate reliably in regulated environments, with human-in-the-loop oversight and rigorous observability.

Join a team delivering scalable AWS-based infrastructure, durable execution, and end-to-end ownership from idea to production.