AI/ML Engineer_L51
Tasks
- Build RAG pipelines
- Build and optimize ETL/ELT pipelines
- Build end to end ML pipelines for training validation deployment monitoring
- Create BI dashboards and analytical insights
- Create guardrail security frameworks
- Deploy cloud native workloads and optimize scalability cost and security
- Design and implement ML and GenAI solutions
- Develop LLM-based applications
- Develop and deploy API-based AI applications
- Implement MLOps workflows for CI CD model registry experiment tracking automated retraining
- Implement prompt engineering and evaluation
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 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 Quality | Data profiling | Databases | Databricks | Dialogflow | Docker | Document Intelligence | ELT | ETL | Embeddings | Experiment tracking | FastAPI | Feature Engineering | Flask | Google ADK | Google Cloud | Google Cloud Storage | Langchain | Langgraph | Looker | MLOps | Model Registry | Observability | Power BI | Prompt engineering | Python | QuickSight | RAG | SQL | Synapse Analytics | 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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