Data & AI Engineer
Tasks
- Build MLOps and LLMOps pipelines
- Build data pipelines using ETL and ELT
- Build lakehouse and warehouse for analytics and AI
- Create curated datasets semantic models and data products
- Define AI data automation architectures
- Deploy AI agents and workflow automation
- Design microservices for scalable automation
- Develop IDP and RAG pipelines
- Drive full lifecycle delivery discovery development testing deployment support
- Enforce security DevOps compliance and data governance
- Ensure data quality lineage observability governance
- Implement GenAI driven architectures and NLP querying
- Implement batch and streaming pipelines
- Implement event-driven architectures
- Implement secure compliant RAG with vector search
- Integrate AI models via APIs and microservices
- Maintain reference architectures and design standards
- Monitor optimize performance reliability and cost
Perks/Benefits
- N/A
Skills/Tech-stack
Airflow | App Services | Azure Container | Azure Container Apps | Azure Cosmos | Azure Cosmos DB | Azure Data | Azure Data Factory | Azure Data Lake | Azure Event | Azure Event Grid | Azure Functions | Azure Kubernetes | Azure Kubernetes Service | Azure ML | Azure OpenAI | Azure Service | Azure Service Bus | Azure Synapse | C# | CI/CD | Container Apps | Cosmos DB | Data Factory | Data Lake | Databricks | Deep learning | Docker | Event Driven | Event Grid | Event-driven architecture | GitHub | Hugging Face | IDP | Java | Keras | Kubernetes | Kubernetes Service | LLMOps | Langchain | MLOps | Machine Learning | NLP | Power Automate | PyTorch | Python | RAG | React | SQL | Scikit-learn | Semantic models | Service Bus | Snowflake | Spark | TensorFlow | Vector Databases | Vector Search | Workato
Education
Roles
AI | AI Engineer | Data Engineer | Engineer | Learning Engineer | Machine Learning Engineer | Software Engineer
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