Machine Learning Infrastructure Engineer - LLM & Agentic AI

Location: United States

Department: 050000 Engineering

Workplace: remote

Employment Type: full

Description

Join a fast-growing global leader in cybersecurity, trusted by some of the biggest names in the industry. In addition to some of the world’s largest enterprises and government agencies, more than 30% of the world’s top MSSPs rely on our platform. We’re at the forefront of protecting organizations against sophisticated cyber threats using cutting-edge AI and automation technologies. Our culture is built on diversity, openness, and collaboration, fostering creativity and innovation that drives real impact in the market.

We are looking for a highly skilled Machine Learning Infrastructure Engineer with a passion for building robust and scalable systems to power Stellar Cyber’s Autonomous SOC applications. In this role, you will be at the forefront of AI infrastructure innovation, responsible for developing the foundational components that enable intelligent agents to work like true SOC analysts to operate in dynamic SecOps environments.

 Responsibilities

• Design and build Agentic AI frameworks to orchestrate LLM-based agents capable of reasoning, planning, and executing SecOps tasks in Stellar Cyber’s Open XDR platform.
• Design and develop scalable LLM inference infrastructure that supports runtime- and cost-efficient serving and resource management across LLM hosting providers.
• Develop MCP servers that interact with Open XDR platform services and features, and integrate with LLM-based agents.
• Develop other supporting API services necessary for Autonomous SOC applications to provide reliable and extensible interface for agent operations.
• Collaborate closely with machine learning / security researchers, UI / backend / infrastructure engineers, and product management to align infrastructure with evolving product needs.

Requirements

• Bachelor’s or Master’s degree in Computer Science or a related field, or equivalent practical experience.
• 2 years of experience with software development or 1 year of experience with an advanced degree in an industry setting.
• 2 years of experience with data structures or algorithms in either an academic or industry setting.
• 2 years of experience with developing backend infrastructure, distributed systems, APIs (REST / gRPC), and microservices for MLOps related projects.
• Experience with cloud computing technologies, including containerization, orchestration, and deployment (Docker, Kubernetes, etc).
• Experience in one or more of the following programming languages: Python, Java, Go.

Preferred Qualifications

• Experience working on LLM applications in high-sensitivity domains (e.g., finance, defense, health).
• Experience with Generative AI frameworks (e.g., LangChain, LangGraph, AutoGen, or other frameworks) and technologies (prompt engineering, vector databases, RAG, etc.).
• Experience with observability practices in ML systems (e.g., metrics, cost tracking).
• Experience deploying and scaling LLMs for inference using frameworks such as vLLM, llama.cpp, and etc.
• Experience working on ML platform teams and/or building tools for researchers.
• Knowledge of SecOps concepts, activities, workflows is a plus.

Benefits

We pride ourselves in recognizing our employees. Here are some examples of our benefits program:

• Pre-IPO Stock Options
• Medical, Dental & Vision care
• 401(k)
• Employee Assistance Program
• Employee Discount Program
• Life Insurance
• Paid time off
• Referral Program
• Rewards and Recognition Program

Why Join Us

• Work at the forefront of cybersecurity innovation within a dynamic, fast-growing team.
• Opportunity to significantly influence and shape the integration architecture of a next-generation SecOps platform powered by AI and automation.
• Competitive salary, comprehensive benefits, and ample career growth opportunities.

The base compensation range for this role is USD 150,000-215,000 per year. Total compensation includes bonus opportunity and equity, and will vary based on candidate location.