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 position is located in London, England. Our office is situated in a core location near Waterloo and Blackfriars on the Southbank. We call our offices Centres of Energy, because they serve as hubs where we accelerate productivity and collaboration, inspire creativity, and cultivate a culture of connection and celebration. Our teams coordinate their time in Centres of Energy to reflect how they work best.

LogicMonitor is proud to be an equal opportunity employer. We deeply care about our employees’ well-being, fostering an environment where every individual is valued and respected. We celebrate the diversity of our team, and are committed to fostering a culture of inclusivity. Come as you are, be yourself, and let's grow together.

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

What You'll Do:

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 sixth year in a row! 

Here's a closer look at this key role:

Develop a deep understanding of the AIOPS problem domain and desired customer outcomes Analyse and measure the effectiveness of current techniques for AIOPS Identify opportunities to improve customer outcomes through leveraging new approaches and algorithms Provide innovative solutions to AIOPS problems, and plan, execute and deliver high quality prototypes to solve these problems Be an active member within the ML Engineering team to develop and scale prototypes to production quality implementation What You'll Need: 3-5 years expertise and applied experience in developing and executing machine learning experiments based on a product or business problem Good programming skills in Python (and/or another coding language) and be able to write clean, maintainable code. Some practical experience with using Generative AI technologies and passionate to explore its potential for improving AIOPS. Be curious and enjoy problem solving both on your own and within a small team Be proactive in diving into the vast amounts of data we have and contribute your ideas within the support of the AIOPS team Practical experience of preparing data for machine learning Excellent written and oral communication skills to be able to present your experiment findings to both the AIOPS team and the wider company  Experience of working with engineering teams to scale prototypes to production quality implementation Batchelor's degree in a numeric discipline (e.g. statistics, machine learning, computer science)

Nice to have

Experience with Generative AI technologies (RAG pipelines, LangChain, Fine tuning LLMs) Experience working with Conversational AI. An understanding of some of the more common machine learning algorithms (e.g., classification, regression, graphs, clustering and NLP techniques) Experience with Docker / Kubernetes Familiar with Atlassian Suite (JIRA, Confluence, Bamboo, BitBucket) An advanced degree (e.g., MSc, PhD) in a numeric discipline (e.g., statistics, machine learning, computer science)  

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