Knowledge of cloud computing principles and expertise in deploying, scaling, and monitoring AI solutions on cloud platforms like Azure.
Knowledge of cloud cost management principles and best practices to optimize cloud resource usage and minimize costs.
Responsibilities
Data Science and AI Development
The Azure ecosystem (including Azure AI Search, Azure Storage Blob, Azure Postgres, and Azure SQL) with expertise in leveraging these tools for data processing, storage, AI models, and analytics tasks.
The Snowflake ecosystem (including Snowflake data warehouse, Snowpark, Snowflake Notebooks, and Streamlit) with expertise in leveraging these tools to create data and AI pipelines.
Python programming is essential, along with familiarity with popular AI
libraries/frameworks such as Langchain, TensorFlow, PyTorch, scikit-learn,
and spaCy.
Proficiency in developing AI solutions using Azure services, including Azure AI Search, Azure OpenAI APIs, and Azure SQL Database.
In-depth knowledge of search algorithms, indexing techniques, and retrieval models for effective information retrieval tasks.
Platforms like Elasticsearch or Azure AI Search is highly advantageous. RAG architecture and its application in natural language processing tasks is a must.
Ability to proficiently manipulate data, perform complex queries, and conduct data transformations using Microsoft SQL Server.
A solid understanding of machine learning and deep learning techniques, particularly those relevant to natural language processing tasks such as text extraction, text classification, sentiment analysis, named entity recognition, and summarization.
Ability to preprocess and clean large datasets efficiently using SQL/Python and other data manipulation tools.
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