About Us:

We love going to work and think you should too. Our team is dedicated to trust, customer obsession, agility, and striving to be better everyday. These values serve as the foundation of our culture, guiding our actions and driving us towards excellence. We foster a culture of performance and recognition, allowing us to transform growth as we enable our employees to do the best work of their careers.

This role is open to candidates based in or near Austin, TX. Our Austin office is based in the vibrant San Jacinto Center downtown with breathtaking views of Lady Bird Lake. At LogicMonitor, we hire within our Centers of Energy—vibrant locations where our teams connect, collaborate, and innovate.

To learn more about life at LogicMonitor, check out our Careers Page.

What You'll Do

LogicMonitor® is the AI-first hybrid observability platform powering the next generation of digital infrastructure. LogicMonitor delivers complete visibility and actionable intelligence across on-premises, cloud, and edge environments. By anticipating issues before they strike, optimizing resources in real time, and enabling faster, smarter decisions, LogicMonitor helps IT and business leaders protect margins, accelerate innovation, and deliver exceptional digital experiences without compromise.

Our customers love LogicMonitor's ability to bring cloud and traditional IT together into one view, as seen in minimal churn rates, expansion business, and exciting new customer references. In fact, LogicMonitor has received the highest Net Promoter Score of any IT Infrastructure Management provider. LogicMonitor also boasts high employee satisfaction. We have been certified as a Great Place To Work®, and named one of BuiltIn's Best Places to Work for the seventh year in a row!

LogicMonitor’s AI Fabric is our enterprise AI operating layer: the shared services and guardrails that let teams build and run AI agents and workflows with trusted context, approved tools, appropriate permissions, and clear quality and operational controls. We’re looking for a hands-on engineer to implement, improve, and support assigned parts of this layer.

You will implement and support assigned services and features. You will investigate problems, propose solutions, and make day-to-day engineering decisions within agreed architecture and standards.

Our environment includes Okta for AI Agents (O4AA), Amazon Bedrock AgentCore, Strands, Model Context Protocol (MCP), agent-to-agent patterns, and workflow orchestration. We do not expect prior experience with every product or protocol. We want someone who understands the concepts, learns unfamiliar systems quickly, and can put them to work reliably.

Here's a closer look at this key role:

Implement and support assigned services in our enterprise AI operating layer for agent execution, context assembly, model routing, tool access, workflow orchestration, and enterprise integrations.
Implement the approved skills-store model: core skills available to everyone, personal skills, team or department skills, and organization-wide skills. Help the shared main agent select skills appropriate to each user and task.
Build self-service features that let users create, test, manage, and use personal skills. Implement the approved review and promotion process for team, department, and organization-wide skills.
Implement skill versioning, selection, testing, auditability, and permission checks. Surface conflicts or policy gaps with options and evidence for the AI Fabric lead and relevant approvers.
Build and use agent harnesses under agreed runtime patterns, including configuration, skill and tool loading, execution controls, tracing, tests, and failure handling.
Develop agents and services in code, using workflow tools where they fit. Integrate APIs, data platforms, tools, and external agents while handling authentication, retries, rate limits, duplicate requests, and recovery.
Apply approved identity and authorization patterns. Implement permission-aware context assembly using approved source-authority, freshness, citation, and context-window rules. Implement approved model routing based on task, policy, quality, latency, and cost, with tests and telemetry for routing decisions.