Machine Learning Engineer

About us:

Radiant Security is an AI-powered SOC co-pilot that enables security operations centers (SOCs) to leverage the power of Gen AI to detect real attacks, reduce remediation times to minutes, and drastically boost analyst productivity. With Radiant, alerts are automatically triaged using AI so that SOCs can eliminate their security alert queues, regardless of their capacity. Uncovered incidents are automatically investigated to determine what happened, what caused it, and to create an incident specific response plan which analysts can launch at the click of a button. With Radiant, SOC teams detect more attacks, respond more rapidly, and get more done.

About the role:

As a Machine Learning Engineer at Radiant Security, you'll be instrumental in designing, developing, and deploying sophisticated AI systems. You will work closely with a cross-functional team to build scalable, efficient, and agile ML solutions that leverage the latest in LLMs, RAG, and more. This is a fantastic opportunity to contribute to groundbreaking AI projects and see your work make a tangible impact.

Responsibilities:

• Design and build scalable machine learning solutions for SaaS applications, focusing on accuracy, efficiency, reliability, and speed.
• Collaborate with the data scientists to refine algorithms and improve model performance based on real-world data and feedback.
• Participate in the entire project lifecycle from research and development to deployment and maintenance of ML models.
• Work on model serving, ensuring models are efficiently deployed and integrated into production environments.
• Manage databases and ensure the integrity and security of data used in training and running ML models.
• Keep abreast of the latest ML technologies and methodologies and propose innovative solutions to enhance project outcomes.
  
  

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Proven experience in machine learning, data science, or AI development.
• Experience with machine learning lifecycle management and LLM deployment strategies
• Experience with SaaS platforms and cloud services (AWS, Google Cloud, Azure).
• Familiarity with cloud services (AWS, Azure, GCP) and managing ML applications in cloud environments
• Excellent problem-solving, analytical, and communication skills.
  
  

• Experience with Large Language Models and Retrieval-Augmented Generation (RAG).
• Knowledge of LLM training and AI agents.
• Experience with model-serving technologies and services
• Experience with automation and orchestration tools, with a focus on enhancing the efficiency of ML workflows
• Prior work in deploying AI/ML models in a scalable, SaaS environment.
• Strong understanding of software development practices and experience with DevOps tools.