Sr. AI Engineer - IT

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.
• Add evaluations, logging, alerts, runbooks, and usage and cost measures to the components you build and support.
• Implement and document components using the platform's established interfaces and patterns. Recommend improvements; the AI Fabric lead decides which changes become shared standards.
• Contribute to bounded pilots for background agents as the operating layer matures, using approved task limits, authorization, monitoring, review points, and outcome checks.

What You'll Need

• 5+ years of relevant hands-on engineering experience, or an equivalent record of delivering substantial backend, platform, infrastructure, AI, or integration systems.
• Strong Python engineering skills and practical experience with APIs, cloud services, containers or serverless runtimes, automated testing, and deployment pipelines. Working ability in JavaScript/TypeScript or SQL is valuable.
• Experience building or operating production services, including monitoring, troubleshooting, incident follow-up, and maintainability improvements.
• Hands-on experience with LLM applications or agents involving tools, retrieval, structured outputs, orchestration, or evaluation. Understand how context selection, model choice, latency, and cost affect the result.
• Experience implementing and building infrastructure in AWS agentcore
• Experience implementing secure integrations using identity, authentication, permissions, secrets management, and audit logging.
• Sound engineering judgment about timeouts, retries, idempotency, state, rate limits, and recovery.
• Ability to turn a defined platform need into a working implementation plan, explain options and evidence, and recognize when an architecture, security, policy, or rollout decision needs review.
• Experience writing meaningful tests and using production evidence to improve variable AI behavior.
• Clear communication with engineering, security, data, and business partners.

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• Anticipated Application Close Date: 01 /11/27