MLOps Cloud Infrastructure Architect
hace 4 días
About Tala
Our MissionWe're on a mission to unleash the economic power of the Global Majority – the 4 billion people overlooked by existing financial systems.
We've been named by the Fortune Impact 20 list, CNBC's Disruptor 50 five years in a row, CNBC's World's Top Fintech Company, Forbes' Fintech 50 list for eight years running, and Chief's The New Era of Leadership Award.
We're expanding across product offerings, countries, and crypto.
The RoleWe're seeking an experienced Senior MLOps Engineer with expertise in designing, implementing, and maintaining suitable infrastructure and best deployment practices of ML Pipelines and models.
You will bring Machine Learning, AI infrastructure, and automation expertise with the knowledge of AWS cloud infrastructure and DevOps practice.
Responsibilities- Design and Build Scalable Infrastructure: Design, build, and maintain scalable and robust infrastructure for AI/ML (Artificial Intelligence / Machine Learning) systems, including cloud-based environments, containerization, and orchestration platforms.
- Implement CI/CD Pipelines: Develop and implement Continuous Integration and Continuous Deployment pipelines to automate the deployment, testing, and monitoring of AI/ML models and applications.
- Evaluate and Integrate Tools: Evaluate and integrate new tools, technologies, and frameworks to improve the efficiency and effectiveness of our MLOps processes.
- Manage Continuous Deployment: Design and manage Continuous deployment using Kubernetes, ArgoCD, and Jenkins.
- Maintain Container Registries: Maintain related container registry and model registry.
- Monitor Infrastructure Utilization: Monitor infrastructure utilization and costs pertaining to model training, inference, and GPU utilization.
- Troubleshoot AI/ML Systems: Monitor and troubleshoot AI/ML systems to ensure high availability, performance, and reliability.
- 4+ years of experience as a DevOps Engineer.
- 1 year of previous experience managing AI/ML infrastructure in public cloud environments.
- In-depth hands-on experience with at least one public cloud platform, preferably AWS.
- Experience with Python or any other programming language.
- Experience with Docker and Kubernetes in production.
- Experience with Continuous Deployment tools such as Jenkins or ArgoCD.
- Experience with Logging and Monitoring tools for SaaS such as Sumo, Splunk, Datadog, etc.
- Proficiency in English.
Estimated Salary: $120,000 - $150,000 per annum.
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