AI Platform Engineer
Tasks
- Administer Amazon EKS clusters
- Automate AI platform tooling with Python
- Build multi tenant multi region AI platform
- Define AI platform architecture
- Deploy CI CD for model experimentation and training
- Design agentic systems with orchestration and safety guardrails
- Design platform self service golden paths
- Develop AI platform roadmap
- Establish governance and guardrails
- Evaluate reliability and cost for AI solutions
- Implement MLOps model pipelines and lifecycle management
- Integrate AI platform with enterprise data ecosystems
- Lead capacity planning for GPU and CPU workloads
- Optimize compute cost and performance
- Serve LLM models with RAG
- Set up observability and monitoring for AI platforms
Perks/Benefits
- Certification support
- Continuous learning
- English training
- Flexible working hours
- Remote work options
- Social benefits package
- Work/life balance focus
Skills/Tech-stack
AWS | Agent Orchestration | Agentic Systems | Amazon EKS | CI/CD | CloudFormation | Compute | GPU Workloads | IAM | Infrastructure as Code | LLM serving | Lifecycle Management | ML pipelines | MLOps | Model Lifecycle | Model Lifecycle Management | Model Registry | Monitoring | Multi-Agent | Multi-agent orchestration | Multi-region | Multi-tenant | Networking | Observability | Prompt Safety | Python | RAG | Rancher | Retrieval-Augmented Generation | Service Mesh | Storage | Terraform | Token aware scaling | Tool use | Tool-use frameworks | Vector Database | “as-code”
Education
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