About the Role
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. 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.
We are seeking an AI/ML Engineer to join the Edwin AI team. In this key role, you will design, build, and deploy AI-powered solutions that enhance observability, automation, and insights across LogicMonitor’s platform. You will be responsible for developing some NLP models, generative AI solutions, and AI-driven analytics that drive automation and efficiency. You will work closely with cross-functional teams, including product, engineering, and infrastructure teams, to integrate state-of-the-art AI models into LogicMonitor’s observability platform.
What You'll Do
- LLM & NLP-Powered Insights for Observability: Build and optimize large language model (LLM)-based solutions that generate human-readable incident reports, root cause explanations, and remediation suggestions. Leverage retrieval-augmented generation (RAG) to surface relevant documentation, logs, and knowledge base insights for IT teams.
- AI-Powered Search & Observability Context Awareness: Develop semantic search and context-aware retrieval solutions to help IT teams quickly find relevant data, logs, and metrics.
- Scalability & Deployment in Cloud Environments: Optimize AI models for real-time performance in distributed, cloud-native environments (AWS, GCP, Kubernetes, etc.).
- Collaboration & Thought Leadership: Work cross-functionally with platform engineers, product teams, and data scientists to align AI-driven features with customer needs.
- AI first attitude: Stay ahead of AI/ML advancements.
What You'll Need
- 2-3 years of experience in AI/ML engineering, with a focus on generative AI, and AI-driven automation and NLP.
- Strong proficiency in Python, Java, or similar languages for AI model development and deployment.
- Hands-on experience with LLMs, RAG (Retrieval-Augmented Generation), and prompt engineering.
- Familiarity in embedding-based search, semantic retrieval, and transformer models (e.g., OpenAI, Hugging Face).
- Familiarity with microservices, REST APIs, and cloud platforms (AWS, GCP, Kubernetes, Docker, etc.).
- Familiarity with observability, ITOps, or monitoring platforms is a plus.
- Strong problem-solving skills with the ability to drive innovation in AI-powered observability solutions.
- Excellent communication and collaboration skills, with experience working in cross-functional teams.