Senior LLMOps / AI Platform Engineer

About the Role
We are looking for a Senior LLMOps / AI Platform Engineer to build, deploy, optimize, and operate production-grade LLM and Generative AI infrastructure. The role combines LLM inference, GPU optimization, Kubernetes, cloud infrastructure, observability, RAG, and AI platform engineering.

Key Responsibilities

• Deploy and operate self-hosted LLMs using vLLM, SGLang, Ollama.
• Optimize LLM inference for latency, throughput, concurrency, GPU memory, KV cache, and cost.
• Manage GPU workloads across multiple NVIDIA GPUs.
• Deploy and maintain AI services on Kubernetes / AWS EKS using Docker and Helm.
• Implement LLM reliability mechanisms including health checks, monitoring, automated recovery, and model restart/refresh strategies.
• Implement observability using Langfuse/LangSmith, OpenTelemetry, Prometheus, and Grafana.
• Deploy and optimize RAG systems, embedding models, and vector databases such as Qdrant, Milvus.
• Support AI agents and workflows built with LangChain and LangGraph.
• Build and maintain CI/CD pipelines for AI services and infrastructure.
• Troubleshoot production issues across LLMs, GPUs, Kubernetes, networking, and AI applications.

Required Skills

• Strong Python and FastAPI experience
• vLLM, Hugging Face and self-hosted LLM deployment
• Kubernetes, Docker, Helm and AWS
• NVIDIA GPU inference and performance optimization
• LangChain / LangGraph/LangSmith
• RAG, embeddings and vector databases (Qdrant)
• LLM observability and monitoring
• PostgreSQL / Redis
• GitHub Actions / CI/CD
• Strong production troubleshooting skills