Applied AI Scientist, GenAI and ML Prototyping
Tasks
- Build rapid AI prototypes
- Define problem statements and success criteria
- Design and run model and LLM evaluations
- Integrate RAG pipelines and agentic workflows
- Lead AI solution discovery and proof of concept
- Manage stakeholder requirements and expectations
- Profile cost and latency for AI feasibility
- Translate prototypes into production ready handoff documentation
Perks/Benefits
Skills/Tech-stack
AWS | Anthropic API | Autogen | Azure | ChromaDB | Classification | Clustering | CrewAI | DeepEval | Docker | Few-Shot Learning | Few-shot | Function Calling | GCP | Git | Google API | Jupyter Notebook | Langchain | Langgraph | Language Processing | Llamaindex | MLflow | Machine Learning | Natural Language | Natural Language Processing | NumPy | OpenAI API | PGVector | Pandas | Pinecone | Prompt engineering | PyTorch | Python | RAG | REST API | Ragas | Regression | SQL | Scikit-learn | Structured Output | TensorFlow | Tool/function calling | TruLens | VS Code | Vector Database | Weaviate | Weights and Biases
Education
Roles
AI Engineer | Applied AI Engineer | Data Scientist | Engineer | Scientist
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