Forward Deployed Engineer (Generative AI)

4 months ago

Remote, United StatesSenior

Responsibilities

Deploy, fine-tune, optimize, and 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 and implement high-throughput data ingestion pipelines and vector database architectures for RAG.
Build resilient, cloud-agnostic deployments across AWS, Azure, and GCP using infrastructure as code.
Serve as a technical consultant on AI safety, prompt engineering, LLM trade-offs, and inference cost optimization.
Share deployment insights with AI research and platform engineering teams to improve product robustness.
Lead high-stakes, on-site customer deployment sprints and debug issues across software and AI layers.

Experienced with LLM orchestration tools such as LangChain, LlamaIndex, and AutoGen, plus PyTorch and Hugging Face.
Production experience with vector databases including Milvus, Pinecone, Qdrant, Chroma, or pgvector.
Proficiency with model serving frameworks such as vLLM, TGI, or Triton Inference Server and evaluation tools.
Advanced knowledge of AWS Bedrock, SageMaker, Azure OpenAI, GCP Vertex AI, and Kubernetes for GPU workloads.
Mastery of Terraform or OpenTofu for provisioning multi-cloud compute environments.
Strong programming skills in Python or Go, including clean concurrent code.
Able to manage customer expectations around LLM non-determinism, hallucinations, and performance trade-offs.
Willingness to travel to client sites.

Remote, full-time position based in the United States.
Opportunity for significant career development in a fast-growing, challenging entrepreneurial environment.
High degree of individual responsibility.
Travel to client sites is required for deployment sprints.

5,001-10,000 employees