Generative AI Tech Lead - RAG & LLMs on GCP

Quantum Technologies. LLC; Rosemead (CA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Quantum Technologies LLC is seeking a senior ML/AI Engineer to design and deploy scalable Generative AI solutions on GCP, focusing on RAG architectures and vector databases. You will build production-grade applications and APIs, and collaborate with cross-functional teams to translate business needs into robust AI roadmaps.

The role requires 5–8+ years in ML, deep experience with LLMs, Vertex AI, LangChain, and containerized CI/CD workflows.

Qualifications

• 5–8+ years in ML with production Generative AI and LLMs.
• Proven experience on GCP and Vertex AI platform.
• Strong Python skills and ML libraries (scikit-learn, PyTorch, TensorFlow).
• Hands-on with LangChain, LlamaIndex, or Hugging Face tools.
• Strong SQL and unstructured data management.
• Containerization and CI/CD with Docker/Kubernetes.

Responsibilities

• Design, build, and maintain production-grade Generative AI apps and APIs on GCP.
• Develop MLOps pipelines for training, evaluation, monitoring, and deployment.
• Improve model performance via fine-tuning, quantization, and prompt engineering.
• Optimize GCP resources for scalability, cost-efficiency, and security.
• Collaborate with Data Science, Data Engineering, and Product teams to translate needs into AI roadmaps.
• Ensure data privacy, security regulations, and ethical AI standards.

Skills

Generative AI

LLMs

Python

SQL

CI/CD

GCP

LangChain

Hugging Face

Kubernetes

Docker

Vertex AI

Tools

LangChain

LlamaIndex

Hugging Face

Vertex AI

BigQuery

Dataflow

Cloud Run

Terraform

Docker

Kubernetes

Job description

Quantum Technologies LLC is seeking a senior ML/AI Engineer to design and deploy scalable Generative AI solutions on GCP, focusing on RAG architectures and vector databases. You will build production-grade applications and APIs, and collaborate with cross-functional teams to translate business needs into robust AI roadmaps.

The role requires 5–8+ years in ML, deep experience with LLMs, Vertex AI, LangChain, and containerized CI/CD workflows.