AI Engineer Global IT Infrastructure
Tasks
- Apply responsible AI bias fairness and privacy checks
- Build retrieval-augmented generation pipelines
- Build train and evaluate ML models
- Conduct exploratory data analysis
- Create AI infrastructure and MLOps pipelines
- Create model cards and data lineage documentation
- Deploy and validate ML models
- Develop genai applications
- Develop tool augmented agents
- Implement infrastructure as code for AI workloads
- Implement monitoring and logging for governance
- Implement prompt engineering patterns
- Manage monitor and troubleshoot deployed models
- Participate in Agile ceremonies and sprint planning
- Perform feature engineering and model experimentation
- Write clean testable well-documented code
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Azure | CI/CD | DVC | Data Analysis | Data Lineage | Docker | Exploratory Data Analysis | Feature Engineering | GCP | Git | Huggingface | Infrastructure as Code | Langchain | Llamaindex | MLOps | MLflow | Machine Learning | Model Cards | Model Deployment | Model Evaluation | Model Monitoring | NumPy | OpenAI | Pandas | Prompt engineering | PyTorch | Python | Retrieval-Augmented Generation | Scikit-learn | TensorFlow | Vector Databases | Weights and Biases | “as-code”
Education
Bachelor of Arts | Bachelor of Engineering | Bachelor of Science
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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