AI Engineer
Tasks
- Architect scalable AI/ML solutions
- Build LLMOps and MLOps pipelines
- Deploy AI models using cloud native services
- Design AI ML target architectures
- Develop and deploy machine learning models
- Ensure secure scalable responsible AI governance
- Implement LLM serving and API integration
- Implement generative AI RAG prototypes
- Implement vector search and knowledge graphs
- Integrate data pipelines for AI workflows
- Mentor team members on AI ML methods
- Monitor model performance and drift
- Perform model evaluation and quality regression testing
- Preprocess structured and unstructured data
- Set up CI/CD and infrastructure-as-code
- Support presales and technical solutioning
Perks/Benefits
Skills/Tech-stack
AI Studio | AWS | AWS Bedrock | AWS SageMaker | Automl | Azure | Azure AI | Azure AI Studio | Azure ML | CI/CD | Databricks | Docker | Document ingestion | Drift Detection | Embeddings | Fine Tuning | Google Cloud | Hugging Face | Human-in-the-loop | Infrastructure as Code | Knowledge graphs | Kubernetes | LLMOps | Langchain | Langgraph | MLOps | MLflow | Machine Learning | Model Evaluation | Model Monitoring | Observability | Prompt engineering | PyTorch | Python | RAG | Scikit-learn | Snowflake | The Loop | Tool-Calling | Vector Databases | Vector Search | Vertex AI | “as-code”
Education
Roles
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