Senior AI Platform Engineer

LogicMonitor; Austin (TX); Hybrid; USD 137,000 - 189,000; Full time

Job summary

LogicMonitor is seeking a hands-on backend engineer to own and advance our enterprise AI operating layer. You will implement, improve, and support assigned services for agent execution, context assembly, model routing, tool access, workflow orchestration, and enterprise integrations in a hybrid Austin environment.

Collaborate with product, security, data, and engineering partners; write meaningful tests; manage authentication, retries, and telemetry; and help define patterns that scale with

Qualifications

• 5+ years of relevant hands-on engineering experience
• 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.

Responsibilities

• 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.
• 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.

Skills

Python

APIs

Cloud services

Containers

Serverless

JavaScript/TypeScript

SQL

LLM apps

Security and IAM

Communication

Tools

AWS

Bedrock AgentCore

Okta

AI frameworks

Terraform

Job description

LogicMonitor is seeking a hands-on backend engineer to own and advance our enterprise AI operating layer. You will implement, improve, and support assigned services for agent execution, context assembly, model routing, tool access, workflow orchestration, and enterprise integrations in a hybrid Austin environment.

Collaborate with product, security, data, and engineering partners; write meaningful tests; manage authentication, retries, and telemetry; and help define patterns that scale with