AI Agent Platform Engineer — Production LLM Systems

STARLIMS Corporation; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

STARLIMS Corporation is seeking a senior software engineer to help build AI-powered agent platforms. You will design runtimes, tool calls, and state management for multi-step agent workflows, integrating with enterprise data and ensuring traceability and validation.

You’ll work on platform and runtime layers, implement human-in-the-loop safeguards, and help drive production-grade reliability across AWS-based services.

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. Agents recommend and draft; people decide
• 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 workflows

Production systems

AWS

TypeScript

Python

Tool calling

Agent platforms

Tools

OpenAI API

Anthropic API

MCP protocol

Job description

STARLIMS Corporation is seeking a senior software engineer to help build AI-powered agent platforms. You will design runtimes, tool calls, and state management for multi-step agent workflows, integrating with enterprise data and ensuring traceability and validation.

You’ll work on platform and runtime layers, implement human-in-the-loop safeguards, and help drive production-grade reliability across AWS-based services.