Role : Gen AI Engineer with GCP
Location : Charlotte NC; Iselin NJ
10 to 15 years of experiance mandatory
- Design and build end-to-end AI/ML systems and applications from experimentation and data preprocessing to production deployment.
- Implement and optimize Generative AI models (text image multimodal) and integrate capabilities like Retrieval-Augmented Generation (RAG) and prompt engineering strategies to enhance LLMs with external knowledge sources.
- Leverage a wide range of GCP services including Vertex AI Big Query Cloud Run GKE (Google Kubernetes Engine) Dataflow and Pub/Sub to build train and deploy custom AI models and solutions.
- Manage the entire model lifecycle including training evaluation fine-tuning versioning deployment and monitoring performance in production environments.
- Optimize models and systems for improved performance scalability efficiency and cost implementing techniques like model quantization and GPU memory optimization.
- Build and maintain scalable and reliable ML pipelines using MLOps practices employing tools like Docker and Kubernetes for containerization and CI/CD pipelines for automated deployment.
- Document technical designs processes and best practices and potentially mentor junior team members
Thanks Regards
Amarythya Sripada
Role : Gen AI Engineer with GCP Location : Charlotte NC; Iselin NJ 10 to 15 years of experiance mandatory Design and build end-to-end AI/ML systems and applications from experimentation and data preprocessing to production deployment. Implement and optimize Generative AI models (text image mult...
Role : Gen AI Engineer with GCP
Location : Charlotte NC; Iselin NJ
10 to 15 years of experiance mandatory
- Design and build end-to-end AI/ML systems and applications from experimentation and data preprocessing to production deployment.
- Implement and optimize Generative AI models (text image multimodal) and integrate capabilities like Retrieval-Augmented Generation (RAG) and prompt engineering strategies to enhance LLMs with external knowledge sources.
- Leverage a wide range of GCP services including Vertex AI Big Query Cloud Run GKE (Google Kubernetes Engine) Dataflow and Pub/Sub to build train and deploy custom AI models and solutions.
- Manage the entire model lifecycle including training evaluation fine-tuning versioning deployment and monitoring performance in production environments.
- Optimize models and systems for improved performance scalability efficiency and cost implementing techniques like model quantization and GPU memory optimization.
- Build and maintain scalable and reliable ML pipelines using MLOps practices employing tools like Docker and Kubernetes for containerization and CI/CD pipelines for automated deployment.
- Document technical designs processes and best practices and potentially mentor junior team members
Thanks Regards
Amarythya Sripada
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