AI/ML Engineer_MS
Tasks
- Build API based AI applications
- Build ETL/ELT pipelines
- Build MLOps workflows for CI CD
- Build RAG pipelines
- Build end-to-end machine learning pipelines
- Create analytics dashboards
- Deploy and optimize AI workloads in cloud environments
- Design and implement genai solutions
- Design and implement machine learning solutions
- Develop data ingestion and feature engineering pipelines
- Develop prompt engineering and evaluation frameworks
- Implement model deployment and monitoring
- Implement model registry and experiment tracking
- Integrate LLMs
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 | 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 | Dialogflow CX | Docker | Document AI | Document Intelligence | ELT | ETL | Embeddings | Experiment tracking | FastAPI | Flask | Google Vertex | Google Vertex AI | JavaScript | Langchain | Langgraph | Looker | MLOps | Model Monitoring | Model Registry | Observability | Plotly Dash | Power BI | Prompt engineering | Python | QuickSight | RAG | SQL | Synapse Analytics | Tableau | ThoughtSpot | Vector Databases | Vertex AI
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI/ML Engineer | Engineer | ML Engineer
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