AI Engineering
Tasks
- Apply prompt engineering for LLMs
- Build LLM-based applications
- Build and manage MLOps workflows
- Build scalable AI services
- Design develop deploy AI solutions
- Develop agentic frameworks
- Develop microservices and APIs
- Implement RAG and fine-tuning
- Implement model tracking and observability
- Monitor and trace model performance
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Airflow | Azure | Azure Data | Azure Data Factory | Azure DevOps | Azure ML | CI/CD | Chroma | Cloud platform | Data Factory | Databricks | Docker | Faiss | FastAPI | Flask | GitHub Actions | Google Cloud | Google Cloud Platform | Jenkins | Kafka | Kubernetes | LLMOps | Langchain | MLOps | MLflow | Microservices | Milvus | Node.js | PGVector | Pinecone | Prompt engineering | PyTorch | Python | RAG | Retrieval-Augmented Generation | SageMaker | Scikit-learn | Synapse | TensorFlow | Vector Databases | Vertex AI
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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