Description

 Design and operate scalable AI infrastructure, including model serving, autoscaling, GPU orchestration, and cost optimization. 

• Build and manage vector database infrastructure for RAG and retrieval workloads, including indexing, sharding, and performance tuning. 
• Implement end-to-end observability, including metrics, tracing, logging, latency/cost dashboards, and alerting for LLM and agent workloads. 
• Build CI/CD and MLOps pipelines for reproducible model and agent deployments with safe rollouts and rollback. 
• Ensure reliability, security, and compliance across the AI serving stack. 
• Partner with ML and agent engineers to remove infrastructure bottlenecks and improve developer velocity. 

Required Skills 

• 3+ years of experience in Platform Engineering, Infrastructure Engineering, DevOps, or MLOps Engineering. 
• Strong experience with cloud platforms (AWS, GCP, or Azure), containers (Docker), and orchestration (Kubernetes). 
• Hands-on experience with model serving technologies such as vLLM, Triton, TGI, or similar, along with inference optimization. 
• Experience operating vector databases such as Pinecone, Weaviate, pgvector, Milvus, or similar. 
• Solid observability and Infrastructure-as-Code (IaC) practices. 
• Proficiency in Python. 

Value Adds 

• Experience with GPU cluster management and cost/performance optimization at scale. 
• Experience supporting production LLM and AI agent workloads. 
• Security and compliance experience for AI systems. 

Core Technology Stack 

• Kubernetes 
• Docker 
• AWS / GCP / Azure 
• Model Serving 
• Vector Databases 
• MLOps 
• Observability 
• Python

Key Skills

Education

Bachelor's degree