LLM Infrastructure & Deployment Summer 2026 Internship¶
Location: Remote (Global)
Time Commitment: ~20 hours/week, 3-4 months
Type: Unpaid Internship
Position Overview¶
We are seeking an LLM Infrastructure Intern with a DevOps mindset to build, deploy, and maintain the production systems that house our large-scale neural models. While our researchers focus on the "what," you will focus on the "how"—ensuring our LLM pipelines are scalable, observable, and cost-effective.
Key Responsibilities¶
- Deploy LLMs and embedding models using containerization (Docker/Kubernetes) and orchestration tools (BentoML, Ray Serve, vLLM)
- Build and automate CI/CD pipelines for model updates
- Administer and optimize production vector databases (Pinecone, Milvus, Qdrant) for high-performance retrieval in RAG systems
- Implement logging and monitoring for LLM performance (Tokens Per Second, latency, model drift)
- Use Infrastructure as Code (Terraform/Ansible) to manage cloud-based GPU clusters
- Develop and maintain FastAPI/Flask wrappers around model endpoints
Qualifications¶
- Strong Python backend development experience with async programming and API design
- Proficiency with Docker and Git; Kubernetes or cloud providers (AWS/GCP/Azure) is a major plus
- Familiarity with inference optimization (quantization, caching, load balancing)
- Experience managing relational and NoSQL/Vector databases
- Ability to troubleshoot complex distributed systems
- Currently pursuing a degree in Computer Science, Software Engineering, or related technical field
Apply Now¶
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