SQL and Python Engineers
Tasks
- Architect and implement AI agents
- Build MLOps pipelines for training versioning and deployment
- Build retrieval-augmented generation pipelines
- Create production feature pipelines
- Deploy models as REST APIs and batch inference services
- Design and deploy large language models
- Develop prompt engineering and evaluation pipelines
- Implement CI/CD pipelines for ML models
- Implement human-in-the-loop checkpoints
- Integrate LLMs with data platforms
- Integrate agents with APIs and enterprise systems
- Manage model registry and lineage tracking
- Mentor junior engineers and define engineering best practices
- Participate in Agile Scrum delivery ceremonies
- Produce technical documentation and deployment runbooks
- Set up model monitoring and data drift detection
- Translate domain requirements into AI solution designs
Perks/Benefits
Skills/Tech-stack
AI Search | Apache Airflow | Azure AI | Azure AI Search | Azure Container | Azure Container Apps | Azure Data | Azure Data Factory | Azure DevOps | Azure Key Vault | Azure Kubernetes | Azure Kubernetes Service | Azure Logic | Azure Logic Apps | Azure Machine Learning | Azure OpenAI | CI/CD | ChromaDB | Container Apps | Data Factory | Databricks | Databricks Feature Store | Databricks Workflows | Delta Lake | Docker | Embeddings | Evidently AI | Feature Store | Git | GitHub Actions | Hugging Face | Hugging Face Transformers | Key Vault | Kubernetes | Kubernetes Service | Langchain | Llamaindex | Logic Apps | MLflow | Machine Learning | NumPy | OpenAI | Pandas | Pinecone | PySpark | PyTorch | Python | Pytorch Transformers | REST APIs | Retrieval-Augmented Generation | SQL | Scikit-learn | Semantic Kernel | Spark SQL | T-SQL | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | Engineer | Learning Engineer | MLOps Engineer | Machine Learning Engineer
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