AI/ML Engineer
Tasks
- Automate retraining pipelines
- Build API based AI applications
- Build ETL/ELT pipelines
- Build RAG pipelines
- Create dashboards and analytical insights
- Create guardrail and security frameworks
- Deploy and monitor ML models
- Design ML and GenAI solutions
- Develop LLM-based applications
- Develop end-to-end ML pipelines
- Implement MLOps workflows for CI CD
- Implement prompt engineering and evaluation
- Optimize scalable secure cost effective deployments
- Set up model registry and experiment tracking
Perks/Benefits
- N/A
Skills/Tech-stack
AI Foundry | AI Search | AWS Bedrock | Agent Engine | Amazon QuickSight | App Service | Azure AI | Azure AI Foundry | Azure AI Search | Azure App | Azure App Service | Azure Blob | Azure Blob Storage | Azure Bot Service | Azure Data | Azure Data Factory | Azure Document Intelligence | Azure OpenAI | Azure Synapse | Azure Synapse Analytics | BigQuery | Blob Storage | CI/CD | Cloud Run | Cloud Storage | Data Factory | Data Governance | Data Lineage | Data profiling | Databricks | Dialogflow | Docker | Document AI | Document Intelligence | ELT | ETL | Embeddings | Experiment tracking | FastAPI | Feature Engineering | Flask | Google ADK | Google Agent Engine | Google Cloud | Google Cloud Storage | Google Cloud Vertex | Google Cloud Vertex AI | JavaScript | Langchain | Langgraph | Looker | Model Deployment | Model Monitoring | Model Registry | Observability | Plotly Dash | Power BI | Prompt engineering | Python | RAG | SQL | Synapse Analytics | Tableau | ThoughtSpot | Vector Search | Vertex AI
Education
N/A
Roles
AI | AI/ML Engineer | Engineer | ML Engineer
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