GCP AI Engineer & Production LLM Architect

GAC Solutions

Pennsylvania

On-site

USD 120,000 - 150,000

Full time

14 days+
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Job summary

A leading AI solutions provider in the United States is seeking a GCP AI Engineer to design, architect, and scale enterprise-grade AI solutions on Google Cloud Platform. Responsibilities include architecting scalable AI/ML solutions, leading technical design reviews, and collaborating on AI research initiatives. The ideal candidate has strong GCP architecture expertise and hands-on experience with production LLMs, along with proficiency in tools like TensorFlow and PyTorch.

Qualifications

  • Strong expertise in GCP architecture and AI services (Vertex AI, Cloud Storage, GKE).
  • Hands-on experience scaling LLMs in production.
  • Proficiency with TensorFlow, PyTorch, Hugging Face, OpenAI, and GCP integrations.
  • Solid background in data preparation, personalization, and experimentation frameworks.
  • Experience with enterprise AI systems and cloud-native design.

Responsibilities

  • Architect and implement scalable AI/ML solutions on GCP (Vertex AI, GKE, BigQuery ML).
  • Design and productionize LLM solutions, including RAG, prompt engineering, and RLHF.
  • Lead technical design reviews and ensure best practices for security, performance, and scalability.
  • Oversee model fine-tuning, deployment, A/B testing, and feedback-driven optimization.
  • Collaborate on open-source initiatives and AI research where applicable.

Skills

GCP architecture
AI services (Vertex AI, Cloud Storage, GKE)
Scaling LLMs in production
TensorFlow
PyTorch
Hugging Face
OpenAI
Data preparation

Job description

A leading AI solutions provider in the United States is seeking a GCP AI Engineer to design, architect, and scale enterprise-grade AI solutions on Google Cloud Platform. Responsibilities include architecting scalable AI/ML solutions, leading technical design reviews, and collaborating on AI research initiatives. The ideal candidate has strong GCP architecture expertise and hands-on experience with production LLMs, along with proficiency in tools like TensorFlow and PyTorch.
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