AI/ML Engineer
Tasks
- Build GenAI powered features prompt engineering RAG LLM integration
- Define production monitoring model quality metrics drift detection alerting
- Deliver model service reference implementations
- Deploy ML GenAI solutions to production and support post launch
- Design, develop, and validate ML models
- Evaluate generative outputs using eval pipelines quality metrics regression detection
- Plan deployments and A B testing
- Scope ML AI initiatives feasibility assessment data requirements technical approach
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | Async Programming | B testing | CI/CD | Causal Inference | Docker | Drift Detection | Feature Engineering | Generative AI | Git | Hugging Face | Hugging Face Transformers | Language Models | Large Language Models | LightGBM | Machine Learning | Model Monitoring | Model Validation | Model calibration | NumPy | Pandas | Polars | Prompt engineering | PyTorch | Python | REST APIs | Retrieval-Augmented Generation | SQL | Scikit-learn | Statistical Analysis | XGBoost
Education
N/A
Roles
AI | AI/ML Engineer | Engineer | ML Engineer
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