AI / ML Engineer
Tasks
- Architect and build large-scale AI systems
- Build agent architectures and tool calling systems
- Build end to end ML platforms for training evaluation deployment monitoring drift detection
- Design and operate workflow oriented AI systems
- Develop data models feature pipelines and APIs
- Diagnose and resolve performance scaling bottlenecks
- Implement low latency inference and continuous learning
- Integrate voice text and event streams with knowledge bases
- Partner with product data science and QA teams to productionize models
Perks/Benefits
- N/A
Skills/Tech-stack
AWS Bedrock | Amazon Kinesis | CI/CD | Data API | Docker | Drift Detection | Event Driven | Event-driven architecture | Feature Store | Google Pub/Sub | Kafka | Kubeflow | Kubernetes | Langgraph | MLOps | MLflow | Model Drift | Model Drift Detection | Model Monitoring | OpenAI API | Pub/Sub | PyTorch | Pydantic | Python | RAG | RabbitMQ | Scikit-learn | Streaming Data | Time Series | Vector Database | Vector Search
Education
N/A
Regions
Countries
States
Cities
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