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.
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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
