Machine Learning Infrastructure Engineer - LLM & Agentic AI
New
S
Stellar CyberCybersecurity, SecOps
United StatesFull-TimeMiddle
SalaryThe 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.
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Job Details
- 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.
- Required Skills
- DockerPythonJavaKubernetesGogRPCRESTful APIsMicroservicesMLOpsDistributed Systems
Requirements
- Hold a Bachelor’s or Master’s degree in Computer Science or a related field, or have equivalent practical experience.
- Have 2 years of software development experience, or 1 year of experience with an advanced degree in an industry setting.
- Have 2 years of experience with data structures or algorithms in an academic or industry setting.
- Have 2 years of experience developing backend infrastructure, distributed systems, APIs (REST / gRPC), and microservices for MLOps-related projects.
- Have experience with cloud computing technologies, including containerization, orchestration, and deployment, such as Docker and Kubernetes.
- Have experience in one or more of Python, Java, or Go.
- Preferred: Have experience working on LLM applications in high-sensitivity domains such as finance, defense, or health.
- Preferred: Have experience with generative AI frameworks such as LangChain, LangGraph, or AutoGen, and technologies such as prompt engineering, vector databases, or RAG.
- Preferred: Have experience with observability practices in ML systems, including metrics and cost tracking.
- Preferred: Have experience deploying and scaling LLMs for inference using frameworks such as vLLM or llama.cpp.
Responsibilities
- Design and build Agentic AI frameworks to orchestrate LLM-based agents that reason, plan, and execute SecOps tasks in Stellar Cyber’s Open XDR platform.
- Design and develop scalable LLM inference infrastructure for 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 supporting API services for reliable and extensible agent operations in Autonomous SOC applications.
- Collaborate with machine learning and security researchers, UI, backend, and infrastructure engineers, and product management to align infrastructure with evolving product needs.
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