Forward Deployed Engineer (Generative AI)

14 days ago

Remote, United StatesSenior

Responsibilities

Deploy, fine-tune, and optimize large-scale generative AI models and LLM orchestration frameworks in customer cloud environments.
Architect scalable AI infrastructure using GPU/TPU orchestration, high-performance storage, and low-latency networking.
Design high-throughput data ingestion pipelines and vector database architectures for retrieval-augmented generation.
Serve as the primary technical consultant for enterprise clients on AI safety, prompt engineering, and inference cost optimization.
Provide deployment insights to AI research and platform engineering teams to improve product robustness.
Debug issues across software layers ranging from GPU drivers to prompt engineering logic.

Advanced knowledge of Vertex AI architecture, including Vertex AI Studio, Model Registry, endpoint deployment, Vertex AI Pipelines, and Vertex AI Vector Search.
Hands-on experience with LangChain, LlamaIndex, AutoGen, PyTorch, and Hugging Face on GCP infrastructure.
Production experience with Vertex AI Vector Search or managed vector stores such as Milvus, Pinecone, and pgvector with Cloud SQL or Spanner.
Proficiency with vLLM, TGI, or Triton Inference Server and automated model evaluation pipelines.
Deep expertise in Google Kubernetes Engine for GPU/TPU workloads, autoscaling, and scheduling.
Mastery of Terraform for provisioning GCP environments, IAM roles, and Vertex AI resources.
Strong programming skills in Python or Go, including clean concurrent code and use of the Google Cloud SDK.
Ability to manage customer expectations around LLM non-determinism, hallucinations, and performance trade-offs.
Willingness to travel to client sites for on-site deployment sprints.

Remote, full-time position with significant career development opportunities and a high degree of individual responsibility.
Willingness to travel to client sites is part of the role.

5,001-10,000 employees