![Tkxel LLC](https://media.trabajo.org/img/noimg.jpg)
AI/ML Workload Manager
1 day ago
Tkxel LLC is a leader in software development, headquartered in Reston, Virginia. Our mission is to deliver innovative software solutions that help prominent enterprises leverage the latest technology advancements to drive business growth.
Job Overview
We are seeking a highly skilled GCP DevOps & Cloud Engineer to support AI/ML projects by designing, implementing, and managing cloud infrastructure on Google Cloud Platform (GCP). This role requires collaboration with data scientists, ML engineers, and software developers to ensure scalable and reliable deployment of AI/ML models.
Responsibilities:- Design, deploy, and manage GCP cloud infrastructure for AI/ML applications.
- Implement IaC using Terraform, Deployment Manager, or Pulumi.
- Manage and optimize AI/ML workloads using Vertex AI, AI Platform, and BigQuery ML.
- Develop and maintain CI/CD pipelines for AI/ML model training and deployment using Cloud Build, Jenkins, or GitHub Actions.
- Implement MLOps practices for automated model versioning, deployment, and monitoring.
- Integrate Kubeflow, TensorFlow Extended (TFX), or MLflow for model lifecycle management.
- Deploy AI/ML applications using Docker and orchestrate with Kubernetes (GKE).
- Optimize containerized AI workloads for scalability and cost efficiency.
- Ensure secure IAM policies, data encryption, and network security best practices.
- Implement audit logging, monitoring, and access control for AI/ML pipelines.
- Comply with GDPR, HIPAA, or industry security standards.
- Set up monitoring with Google Cloud Operations Suite (Stackdriver), Prometheus, and Grafana.
- Optimize compute, storage, and networking costs for AI workloads.
- Implement logging and alerting strategies for AI model performance monitoring.
- AI Compute & Storage: AI Platform, BigQuery ML, Cloud TPU, DataFlow, Dataproc.
- Model Deployment: TensorFlow Serving, TorchServe, FastAPI for AI models.
Requirements
- 5+ years of relevant experience.
- Professional Cloud DevOps Engineer or Professional Cloud Architect.
- Nice to have: Professional Machine Learning Engineer.
- Experience working with AI/ML workloads on Google Cloud.
- Strong knowledge of scaling AI models, optimizing ML training jobs, and managing feature stores.
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