AI/ML Engineer
Tasks
- Build RAG pipelines and LLM integrations
- Build end-to-end ML pipelines
- Create dashboards and analytical insights
- Deploy and monitor AI workloads on cloud services
- Design and implement ML and GenAI solutions
- Develop ETL/ELT pipelines
- Develop and deploy API-based AI applications
- Ensure scalable secure and cost optimized deployments
- Implement MLOps workflows with CI CD
Perks/Benefits
- N/A
Skills/Tech-stack
AI Foundry | AI Search | AWS Bedrock | App Service | Automated retraining | 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 | BigQuery | Blob Storage | CI/CD | Cloud Run | Cloud Storage | Data Factory | Data Governance | Data Quality | Data profiling | Databricks | Dialogflow | Docker | Document Intelligence | ELT | ETL | Embeddings | Experiment tracking | FastAPI | Feature Engineering | Flask | Google Cloud | Google Cloud Storage | Guardrails | JavaScript | Langchain | Langgraph | LogProbs | Looker | MLOps | Model Monitoring | Model Registry | Power BI | Prompt engineering | Python | QuickSight | RAG | SQL | Tableau | Vector Search | Vertex AI
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer
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