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The Senior Cloud Optimization Consultant will develop and implement a hybrid cloud strategy, optimize cloud operations, and ensure efficient resource management while collaborating with teams.
The Company
Outrider is a software company that is automating distribution yards with electric, self-driving trucks. Our system eliminates manual tasks that are hazardous and repetitive while it improves safety and efficiency. Outrider’s mission is to drive the rapid adoption of sustainable freight transportation. We are a private company founded in 2018 and backed by NEA, 8VC, Koch Disruptive Technologies, NVIDIA, and other top-tier investors. Our customers are Fortune 200 companies and our autonomous trucks are already running in distribution yards. For more information, visit www.outrider.ai
Responsibilities:
- Develop and Optimize Hybrid Cloud Strategy: Devise a cost-effective hybrid cloud strategy that directly supports and achieves Outrider's business objectives.
- Collaborate on Implementation: Partner with Outrider team members to successfully implement the defined hybrid cloud strategy.
- Ensure Efficient Operations: Work alongside Outrider team members to operate and maintain the hybrid cloud environment for maximum efficiency.
Qualifications:
- Cloud Infrastructure and Architecture:
- Deep expertise in major public cloud platforms (e.g., AWS, Azure, Google Cloud) and on-premises infrastructure including Nvidia DGX and GPU servers
- Proven experience in designing, implementing, and optimizing hybrid cloud architectures
- Deep understanding of containerization and orchestration technologies (e.g., Docker, Kubernetes)
- Demonstrable success in identifying and achieving significant cost savings in hybrid cloud environments.
- Expertise in FinOps principles, including cost allocation, budgeting, and forecasting.
- Ability to analyze cloud billing data and utilization reports to pinpoint inefficiencies.
- Experience with automated cost management tools and cloud-native cost monitoring services.
- Experience optimizing the infrastructure and resource consumption for MLOps components, including model training, serving, and monitoring.
- Understanding of efficient resource scaling for machine learning workloads, including GPU utilization optimization.
- Familiarity with cost implications of various ML frameworks and platforms (e.g., TensorFlow, PyTorch, Vertex AI, SageMaker).
- Excellent communication skills with the ability to articulate complex technical and financial concepts to both technical and executive audiences.
- Proven ability to work collaboratively with cross-functional teams (Engineering, Finance, Data Science).
- Experience in developing clear, actionable recommendations and implementation plans.
- A portfolio demonstrating successful cost optimization projects, including realized savings metrics.
Assignment:
- 3-month contract with possibilities to extend
Top Skills
AWS
Azure
Docker
GCP
Gpu Servers
Kubernetes
Nvidia Dgx
PyTorch
Sagemaker
TensorFlow
Vertex Ai
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