Machine Learning Engineer
Tasks
- Build generative AI solutions using large language models
- Build time series forecasting solutions
- Conduct A B testing for model effectiveness
- Deploy production machine learning APIs and services
- Design predictive machine learning models for business use cases
- Develop automated model retraining workflows
- Develop machine learning pipelines for training and deployment
- Implement MLOps for model versioning and monitoring
- Implement model monitoring for accuracy and drift
- Implement retrieval-augmented generation pipelines
- Maintain model documentation and deployment standards
- Optimize model performance using experimentation
- Participate in technical design and code reviews
- Perform data preprocessing and feature engineering
- Perform hyperparameter tuning and cross validation
- Troubleshoot model data pipeline and production issues
Perks/Benefits
Skills/Tech-stack
APIs | AWS | AWS Lambda | AWS SageMaker | AWS Step Functions | Amazon CloudWatch | Amazon ECR | Amazon ECS | Amazon EKS | Amazon S3 | Automated testing | Backtesting | Bias | CI/CD | Chunking | Concept drift | Cross-validation | Data Drift | Data Versioning | Docker | Document processing | Embeddings | Experiment tracking | Explainability | Feature Engineering | Generative AI | Git | Hyperparameter Tuning | Infrastructure as Code | Kubernetes | LLM Evaluation | Langchain | Language Models | Large Language Models | MLOps | Machine Learning | Model Evaluation | Model Monitoring | Model Registry | Model Training | Prompt engineering | PyTorch | Python | Reproducible ML | Retrieval-Augmented Generation | SQL | Scikit-learn | Series forecasting | Step Functions | TensorFlow | Time Series | Time Series Forecasting | Vector Databases | Vector Search | XGBoost | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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