LLM Infrastructure Engineer

Role Overview:

• Build and scale infrastructure for LLM training, fine-tuning, and inference.
• Work with ML and engineering teams to ensure reliable, efficient production systems.

Key Responsibilities:

• Design and maintain infrastructure for LLM workloads; manage GPU compute and optimize utilization.
• Deploy inference services with focus on latency, throughput, and reliability.
• Automate deployments with IaC and CI/CD; implement monitoring and cost optimization.

Required Qualifications:

• Proven experience with LLM infrastructure and production ML systems.
• Strong Python, distributed systems, and container orchestration with Docker and Kubernetes.
• Prior remote work experience and familiarity with US/UK companies.