An AI engineer in Nepal spends their week primarily on tasks such as verifying model outputs, cleaning and preparing data for ingestion, and optimizing code for efficiency and cost. Contrary to common perceptions, the role involves less focus on training models from scratch and more on integrating existing LLMs like those from OpenAI, Anthropic, and Google with frameworks such as LangChain. Key responsibilities include ensuring answer accuracy using tools like RAGAS, guarding against prompt injection attacks, and meticulously documenting changes and test results. AI
IMPACT Highlights the practical, integration-focused aspects of AI engineering roles, emphasizing data cleaning and prompt engineering over model training.
RANK_REASON Article discusses the day-to-day realities of an AI engineering role, contrasting it with common career perceptions, rather than announcing a new product or research.
- Anthropic
- Git
- Kathmandu
- LangChain
- langgraph
- LlamaIndex
- Nepal
- NumPy
- OpenAI
- OWASP
- Pandas
- Python
- Ragas
- SQL
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