Machine Learning Eng. (GenAI & Enterprise Systems)
Tasks
- Add guardrails for ML and LLM systems
- Build data and model pipelines
- Collaborate with data scientists to productionize prototypes
- Design and implement end-to-end ML systems
- Develop scalable RAG architectures
- Engage with technical teams and clients
- Implement automated testing and evaluation
- Implement model serving and API integration
- Optimize model performance for latency, throughput, and cost
- Productionize machine learning models
Perks/Benefits
- N/A
Skills/Tech-stack
APIs | AWS | Automated testing | Azure | CI/CD | Cloud platform | Distributed Systems | Evaluation Frameworks | Google Cloud | Google Cloud Platform | Language Models | Large Language Models | Machine Learning | Model Deployment | Model Monitoring | Model Serving | Observability | PyTorch | Python | RAG | Retrieval-Augmented Generation | Scalable Architecture | Scikit-learn | TensorFlow
Education
N/A
Regions
Countries
States
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