Applied AI Engineer

LogicMonitor•San Francisco, CA

12d•Hybrid

About The Position

LM Envision, LogicMonitor's leading hybrid observability platform powered by AI, helps modern enterprises gain operational visibility into and predictability across their IT stacks, so they can continue to deliver extraordinary employee and customer experiences. LogicMonitor has a layered approach to intelligence, where AI and Machine Learning is baked into every facet of the LM Envision platform to help IT teams improve efficiency, minimize alert fatigue, proactively predict trends, and maximize enterprise growth and transformation. 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! We’re looking for an Applied AI Engineer who lives at the intersection of product, AI engineering, and customer success. You’ll ensure AI capabilities are not just deployed, but adopted, robust, and deeply valuable in customer environments. You’ll be a technical partner internally and externally, owning the loop from prototyping to metrics to insights to roadmap feedback. This is not a traditional support or operations role. You’ll build reliable systems, define evaluation signals, influence model and product behavior, and scale AI adoption across customers. Here's a closer look at this key role:

Requirements

2-3 years in a customer-facing technical role: solutions engineering, forward-deployed engineering, AI/ML ops, or applied research + deployment.
Strong solution architecture in design enterprise software systems and AIOps systems
Hands-on experience with LLMs / generative AI / agent frameworks (prompting, retrieval, chaining, evaluation).
Ability to design evaluation metrics, error analysis, and performance monitoring.
Strong communication: you can explain complex AI ideas to engineers, product folks, or non-technical stakeholders.
Comfortable working in ambiguity, balancing trade offs, managing multiple stakeholders.

Nice To Haves

Proficiency scripting in Python with AI/LLM tooling and system observability/integration workflows (e.g. API debugging with Postman) preferred.

Responsibilities

Ensure customer success post-deployment: Drive adoption, measure value realization and provide best practices on maximizing impact of AI capabilities.
Act as technical owner: Serve as the go-to technical advisor for customers, designing custom configurations, integrations, and prompt or agent workflows tailored to their environments.
Prototype and evaluate: Build proofs-of-concept with customers, define evaluation metrics, run experiments, and analyze errors to improve model and feature quality.
Drive product improvements: Translate customer feedback and production learnings into clear, actionable requests for Product and Engineering.
Instrument reliability and observability: Develop health monitors, logging, and drift detection to ensure AI performance remains stable and measurable in production.
Resolve and automate: Investigate production issues across data, APIs, and model behavior; create durable fixes and automation to prevent recurrences.
Enable and educate: Lead workshops, demos, and internal enablement sessions to build confidence and understanding of Edwin AI’s capabilities.
Stay current: Explore and apply emerging techniques in LLMs, generative AI, agent frameworks, and evaluation methods to push the product forward.