Forward Deployed Engineer (Staff)

What you'll do

• Write production code and build prototype integrations directly in customer environments to resolve technical hurdles.
• Architect, deploy, and maintain production-grade vSphere Kubernetes Service (VKS) clusters for general compute and GPU workloads.
• Lead the migration of large-scale bare metal environments and legacy data stacks to cloud-native Kubernetes targets.
• Deploy and scale AI inferencing workloads, RAG architectures, and vector search frameworks using virtualized GPU resources.
• Containerize and migrate heavy stateful engines, including message brokers like Kafka and distributed caches like Redis.
• Identify recurring migration blockers and develop field fixes to accelerate software iteration cycles.
• Translate field-tested code and customer pain points into prioritized feature requirements for the core engineering team.
• Manage complex infrastructure deployments using Terraform, Helm, Kubernetes Operators, and GitOps tools.

What we're looking for

• Must have at least 12 years of related experience or a Bachelor's degree.
• Experience in Kubernetes, platform modernization, large scale stateful workload migration, and enterprise AI infrastructure.
• Hands-on experience operating GPU accelerated Kubernetes nodes and model serving runtimes like vLLM, TGI, and Triton Inference Server.
• Expertise in vector databases (Milvus, Qdrant, Pgvector) and distributed AI orchestration using Ray or KubeRay.
• Experience containerizing and migrating stateful engines including Redis, Oracle Coherence, Hazelcast, Apache Kafka, and RabbitMQ.
• Proficiency with cloud-native data stacks including Spark, Flink, Dask, Airflow, Trino/Presto, and Cassandra/ScyllaDB.
• Advanced skills in Infrastructure as Code and GitOps using Terraform, Helm, Kubernetes Operators, Cluster API, ArgoCD, or Flux.
• Technical depth across multiple Kubernetes distributions such as OpenShift, EKS, GKE, AKS, or Rancher RKE/RKS.