Solutions Architect - LLM and LangChain

Responsibilities:

1. Building LLM-Powered Applications with LangChain.

Required Knowledge:

1. Good knowledge on the fundamentals of Language Models (LLMs) and their significance in building intelligent applications.
2. Hands-on experience with in-context learning, domain-specific model fine-tuning, and prompt engineering.
3. Experience in integration of LangChain into application development workflows and utilize its APIs.
4. Insights on what can be done with LangChain.
5. Interact with various LLMs using LangChain.
6. Create a conversational chatbot using LLM.
7. Fine-tuning LLM with LangChain.
8. Experience with Python.
9. Experience with tools and frameworks popular in the LLM and NLP communities, such as LangChain, LlamaIndex, PyTorch, and TensorFlow.
10. Familiarity with Linux operating systems and containerization technologies like Docker and Kubernetes.

Educational Background:

1. Master's in Computer Science, Data Science, or a related field.

Programming Skills:

1. Proficiency in statistical programming languages such as R and Python, as well as database query languages like SQL.

Statistical Expertise:

1. Solid understanding of applied statistics, including but not limited to statistical tests, distributions, regression analysis, and maximum likelihood estimators.

Machine Learning:

1. Strong grasp of machine learning algorithms including k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests, and Neural Networks.