GenAI Architect & RAG Lead (GCP)

Ethereum Technologies LLC; Rosemead (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Ethereum Technologies LLC is seeking a senior ML engineer to design and deploy scalable Generative AI solutions in production on Google Cloud Platform, emphasizing RAG architectures and vector databases.

You will lead MLOps pipelines (training, evaluation, monitoring, deployment) with Vertex AI, Kubeflow, Cloud Build, and Terraform, and collaborate with data science and product teams to align AI roadmaps with business needs.

Qualifications

• 5–8+ years in ML engineering and deployment.
• 3+ years deploying Generative AI in production.
• Experience with GCP AI suite and Vertex AI.
• Strong Python and ML libraries (scikit-learn, TensorFlow, PyTorch).

Responsibilities

• Design, build, and maintain production AI apps and APIs on GCP, focusing on Gemini models, RAG, and vector stores.
• Develop automated MLOps pipelines (training, evaluation, monitoring, deployment) using Vertex AI, Kubeflow, Cloud Build, Terraform.
• Implement fine-tuning, quantization (GPTQ, AWQ), and prompt engineering to boost accuracy and reduce latency.
• Optimize GCP resources for HPC, ensuring scalability, cost-efficiency, and security (IAM, VPC).
• Collaborate with Data Science, Data Engineering, and Product teams to translate business needs into AI roadmaps.
• Ensure compliance with data privacy, security regulations (HIPAA, GDPR) and ethical AI standards.

Skills

Generative AI

GCP Vertex AI

Python

LangChain

LlamaIndex

Hugging Face

SQL

Tools

Docker

Kubernetes (GKE)

CI/CD

BigQuery

TensorFlow

PyTorch

Job description

Ethereum Technologies LLC is seeking a senior ML engineer to design and deploy scalable Generative AI solutions in production on Google Cloud Platform, emphasizing RAG architectures and vector databases.

You will lead MLOps pipelines (training, evaluation, monitoring, deployment) with Vertex AI, Kubeflow, Cloud Build, and Terraform, and collaborate with data science and product teams to align AI roadmaps with business needs.