GenAI/ML Engineer
Tasks
- Build end-to-end data pipelines
- Build feature engineering workflows
- Collaborate with compliance stakeholders
- Create audit-ready documentation
- Deploy machine learning models to production
- Develop GenAI RAG systems
- Develop agentic workflows
- Develop ensemble machine learning models
- Integrate llm solutions
- Manage multi environment releases
- Monitor model drift
- Perform hyperparameter optimization
- Run cross validation
- Track model performance
- Train validate machine learning models
Perks/Benefits
Skills/Tech-stack
Agentic Workflows | Apache Airflow | Apache Spark | CI/CD | Cross-validation | Docker | Drift Detection | Ensemble learning | Feature Engineering | Hyperparameter Optimization | Kubernetes | LLM integration | Language Models | Large Language Models | Machine Learning | Model Drift | Model Drift Detection | Model Explainability | Neural Networks | Out of Time Testing | Prompt engineering | Python | RAG | Retrieval-Augmented Generation | Shapley Values | Supervised Learning | Tree-based Models | Unsupervised Learning
Education
N/A
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