AI/ML Engineer_L51
Tasks
- Build RAG pipelines
- Build and deploy end-to-end ML pipelines
- Create dashboards and analytical insights
- Design and implement ML and GenAI solutions
- Develop API based AI applications
- Develop ETL/ELT pipelines
- Ensure scalable secure cost optimized AI deployments
- Implement MLOps workflows with CI CD
- Integrate LLM based applications
- Monitor models and production workloads
Perks/Benefits
- N/A
Skills/Tech-stack
AI Foundry | AI Search | AWS Bedrock | App Service | Azure AI | Azure AI Foundry | Azure AI Search | Azure App | Azure App Service | 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 | Databricks | Dialogflow | Docker | Document AI | Document Intelligence | ELT | ETL | Embeddings | Experiment tracking | FastAPI | Flask | Google Cloud | Google Cloud Run | Google Vertex | Google Vertex AI | JavaScript | Langchain | Langgraph | Looker | MLOps | MLflow | Model Registry | Power BI | Prompt engineering | Python | QuickSight | RAG | SQL | Tableau | ThoughtSpot | Vector Search | Vertex AI
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI/ML Engineer | Engineer | ML Engineer
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