- SRE Fundamentals: 4+ years of experience in SRE, DevOps, or Production Engineering roles supporting high-traffic, user-facing applications.
- LLMOps / AI Infrastructure Expertise: Experience working with AI workloads (such as serving models using vLLM, Server TGI or working with cloud providers like Bedrock, OpenAI, etc.).
- Container Orchestration: Strong expertise in Kubernetes (EKS, GKE, or AKS) and infrastructure-as-code (Terraform).
- AI/ML Ecosystem: Hands-on experience with inference servers (e.g., vLLM, TGI) and vector databases (e.g., Pinecone, Milvus, Qdrant).
- Programming: Proficiency in Python and Go for automation, tooling, and backend optimization.
- Cloud Architecture: Deep experience managing cloud compute resources, specifically specialized GPU instances
- Models AI and Code:
Ability to build automation processes that not only update code versions, but also support testing and safe deployment of new models (Shadow Deployments, Canary releases for models) Product thinking and user orientation (User-Facing)
Mastery of tools like OpenTelemetry, Prometheus, Datadog or Grafana, with the ability to trace agent-based systems and complex model calls.
- Cost & Capacity Optimization:
Ability to manage the high costs of GPU/Inference in a productive architecture without compromising availability or performance.
- Empathy for the end-user experience:
Understanding that every millisecond of latency or error in the stream directly impacts customer retention.
Experience building semantic caching layers to reduce LLM API costs. Active contributor to open-source LLMOps or MLOps projects. (edited)
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