Description

Key Responsibilities
• AI Infrastructure Reliability: Design, scale, and maintain highly available infrastructure for LLM training, fine-tuning, and inference workloads.
• Agentic Operations: Architect and deploy multi-agent GenAI systems to automate alert triage, root cause analysis (RCA), and self-healing system remediation.
• GPU & Cluster Management: Optimize GPU orchestration, cluster health, and compute utilization across large-scale Kubernetes clusters.
• Performance Monitoring: Define and monitor non-traditional SLOs/SLIs, including Time-to-First-Token (TTFT), Inter-Token Latency, and cost-per-query limits.
• Data & Vector Pipeline Ops: Ensure the reliability, latency, and synchronization of vector databases and Retrieval-Augmented Generation (RAG) pipelines.
• Incident Management & ChatOps: Integrate LLMs and agentic frameworks into ChatOps tooling (e.g., Slack, Teams) to provide real-time, natural-language incident assistance.
• Security & Guardrails: Implement infrastructural boundaries to protect LLM endpoints from prompt injections, hallucinations, and data compliance leaks.
Required Technical Skills
• Infrastructure & DevOps: Deep expertise in Kubernetes (EKS/GKE), Infrastructure as Code (Terraform), and CI/CD deployment pipelines.
• Software Engineering: Strong proficiency in Python or Go, with experience building tool integrations via APIs and Model Context Protocol (MCP).
• GenAI Engineering: Hands-on experience with LLM orchestration frameworks (e.g., AutoGen, LangChain, LlamaIndex).
• Data & Vector Systems: Experience managing distributed vector databases (e.g., Pinecone, Milvus, Qdrant, or pgvector).
• Observability: Advanced knowledge of cloud monitoring stacks (Datadog, Prometheus, OpenTelemetry) applied to both standard infrastructure and AI workloads (e.g., Triton Inference Server monitoring).
Preferred Qualifications

Key Skills

Education

Bachelor's degree