Principal Machine Learning Engineer
Tasks
- Architect MLOps platforms
- Build agentic AI systems
- Build production ML models
- Design retrieval-augmented generation systems
- Develop graph neural network models
- Ensure data governance and compliance
- Implement CI/CD pipelines
- Implement feature and vector stores
- Lead training evaluation and serving pipelines
- Monitor model drift and performance
- Orchestrate agents and workflows
- Set engineering quality and evaluation standards
Perks/Benefits
Skills/Tech-stack
AWS | Agent systems | Argo Workflows | ArgoCD | CI/CD | Data Governance | Data Processing | Docker | ETL | Entity Resolution | Feature Store | GCP | GRPC | Grafana | Graph Neural Networks | Kafka | Knowledge graphs | Kotlin | Kubernetes | Language Models | Large Language Models | Link Analysis | MLOps | Machine Learning | Model Monitoring | Multi-Agent | Multi-Agent Systems | Neural Networks | PostgreSQL | Python | React | Retrieval-Augmented Generation | Streaming Data | Streaming data processing | TypeScript | Vector Store | Yugabyte
Education
N/A
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