Machine Learning Engineer
Tasks
- Apply security and responsible AI practices
- Build AI and GenAI applications
- Collaborate with data scientists for production transition
- Develop end-to-end ML pipelines
- Develop reusable ML platform components
- Document technical designs and operational procedures
- Evaluate and benchmark machine learning algorithms
- Implement CI/CD practices
- Implement genai solutions
- Implement monitoring and observability
- Perform code reviews and testing
- Perform exploratory data analysis and feature engineering
- Productionize machine learning and GenAI models
- Troubleshoot production ML systems
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Access Control | Agent Frameworks | Amazon Bedrock | Automated testing | CI/CD | Classification | Clustering | Data Drift | Data Lineage | Deep learning | Dimensionality Reduction | Docker | Embeddings | Encryption | Experiment tracking | GRPC | Git | Java | Kubeflow | Kubernetes | Langchain | Language Models | Large Language Models | MLOps | MLflow | Machine Learning | Microservices | Model Drift | Model Explainability | Model Monitoring | Model Registry | NoSQL Databases | Object-Oriented | Object-oriented programming | Prompt engineering | Python | REST APIs | Regression | Relational databases | Retrieval-Augmented Generation | SageMaker | Time Series | Vector Databases
Education
N/A
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