E-IT
Global Client Partner- USA, CANADA & LATAM Recruitment @ E-IT Professionals
Role: Gen AI Engineer with GCP
Contract
Must have: Gen AI, LLM, RAG, MLOps, Vertex AI, GCP experience
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.
Seniority level Mid-Senior level
Employment type Contract
Job function Information Technology
Referrals increase your chances of interviewing at E-IT by 2x
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Contract
Must have: Gen AI, LLM, RAG, MLOps, Vertex AI, GCP experience
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.
Seniority level Mid-Senior level
Employment type Contract
Job function Information Technology
Referrals increase your chances of interviewing at E-IT by 2x
#J-18808-Ljbffr