AI/ML Engineer
Tasks
- Build RAG pipelines and LLM integrations
- Build and optimize ETL and ELT pipelines
- Build end to end ML pipelines for ingestion feature engineering training validation deployment and monitoring
- Create BI dashboards and analytical insights
- Deploy and monitor scalable secure and cost optimized AI workloads
- Design and implement ML and GenAI solutions
- Develop and deploy API-based AI applications
- Implement MLOps workflows with CI CD model registry and experiment tracking
- Implement guardrail and security frameworks
- Perform prompt engineering and evaluation
Perks/Benefits
- N/A
Skills/Tech-stack
AI Foundry | AI Search | AWS Bedrock | Amazon QuickSight | 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 | Azure Synapse Analytics | Blob Storage | CI/CD | Cloud Run | Cloud Storage | Data Factory | Databricks | Docker | Document AI | Document Intelligence | ELT | ETL | Embeddings | Experiment tracking | FastAPI | Feature Engineering | Flask | Google BigQuery | Google Cloud | Google Cloud Run | Google Cloud Storage | Google Document AI | Google Vertex | Google Vertex AI | JavaScript | Langchain | Langgraph | LogProbs | Looker | MLOps | Machine Learning | Model Registry | Power BI | Prompt engineering | Python | RAG | SQL | Synapse Analytics | Tableau | ThoughtSpot | Vector Retrieval | Vector Search | Vertex AI
Education
N/A
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer
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