GenAI/ML Engineer
Tasks
- Build end-to-end data pipelines
- Build feature engineering workflows
- Collaborate with compliance and engineering stakeholders
- Deploy models to production
- Design supervised unsupervised and ensemble models
- Develop GenAI agentic workflows
- Implement CI/CD pipelines
- Implement RAG systems
- Integrate LLM-based automation
- Manage multi environment releases
- Monitor model drift and performance
- Orchestrate data pipelines with Airflow
- Produce audit-ready documentation
- Train calibrate validate machine learning models
Perks/Benefits
- Inclusive work environment
- Knowledge transfer
- Long term enablement
- Professional documentation support
Skills/Tech-stack
Agentic Workflows | Apache Airflow | CI/CD | Cross-validation | Drift Detection | Ensemble learning | Experiment design | Feature Engineering | Hyperparameter Optimization | Kubernetes | Language Models | Large Language Models | Machine Learning | Model Explainability | Neural Networks | Out of Time Testing | Prompt engineering | Python | RAG | Retrieval-Augmented Generation | SHAP | Spark | Statistical modeling | Supervised Learning | Tree-based Models | Unsupervised Learning
Education
N/A
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