AI Engineer
Tasks
- Assess data infrastructure data governance and data quality
- Collaborate with AI production team for integrations
- Define AI/ML problem statements
- Define business case ROI and measurable benefits
- Design end-to-end AI solutions
- Develop AI ML prototypes and proofs of concept
- Evaluate AI readiness and recommend data improvements
- Identify client AI opportunities
- Implement MLOps workflows for production
- Research and evaluate generative AI and LLMs
Perks/Benefits
- N/A
Skills/Tech-stack
BigQuery | Cloud Data Catalog | Cloud Functions | Cloud Storage | Cloud platform | Data Architecture | Data Catalog | Data Governance | Data Quality | Dataflow | Deep learning | Distributed Computing | Docker | GKE | Generative AI | Google Cloud | Google Cloud Data | Google Cloud Data Catalog | Google Cloud Platform | Great Expectations | Kubernetes | Langchain | Language Models | Large Language Models | MLOps | MLflow | Machine Learning | Model Registry | Parallel processing | PyTorch | Python | Scikit-learn | TensorFlow | Vertex AI
Education
Roles
AI | AI Engineer | Engineer
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