Job Description
Role Overview: AI Specialist
- Team: Business Intelligence and AI Team.
- Focus: Enhancing data accessibility, conversational AI, and building AI-driven workflows.
- Goal: Optimize internal operations using AI solutions before expanding externally.
- Experience Level: 5+ years of overall experience.
- Leadership Quality: Some leadership qualities are expected.
Technical Requirements
- Core Skills: Experience delivering AIML production systems, conversational AI, LLMs, GPT models.
- Programming: Python and AIML libraries.
- Cloud/Platforms: Azure AI, Snowflake.
- Key Concept: Agent workflow – automating tasks by chaining AI agents (e.g., for recruitment sourcing).
- Traditional ML: Ability to build, train, and deploy custom ML models.
- Vector Database: Hands-on experience with vector databases for semantic search and efficient data retrieval in RAG implementations.
- Snowflake Cortex: Strong experience in Snowflake, specifically Snowflake Cortex for building internal UIs and applications. 1-2 years hands-on experience in Snowflake is sufficient.
- LLMs Used: Azure AI (GPT-4, GPT 4.2), exploring Gemini Pro. Traditional models include Llama-based models (e.g., 70 billion parameters).
- Data Sources: Primarily Snowflake data lake, potentially Azure and AWS S3.
- Financial Background: Good to have experience in financial or regulated industries, or an understanding of finance-related business questions.
- Testing: Basic unit testing and generation of unit test cases are required.
- Team: Business Intelligence and AI Team.
- Focus: Enhancing data accessibility, conversational AI, and building AI-driven workflows.
- Goal: Optimize internal operations using AI solutions before expanding externally.
- Experience Level: 5+ years of overall experience.
- Leadership Quality: Some leadership qualities are expected.
Technical Requirements
- Core Skills: Experience delivering AIML production systems, conversational AI, LLMs, GPT models.
- Programming: Python and AIML libraries.
- Cloud/Platforms: Azure AI, Snowflake.
- Key Concept: Agent workflow – automating tasks by chaining AI agents (e.g., for recruitment sourcing).
- Traditional ML: Ability to build, train, and deploy custom ML models.
- Vector Database: Hands-on experience with vector databases for semantic search and efficient data retrieval in RAG implementations.
- Snowflake Cortex: Strong experience in Snowflake, specifically Snowflake Cortex for building internal UIs and applications. 1-2 years hands-on experience in Snowflake is sufficient.
- LLMs Used: Azure AI (GPT-4, GPT 4.2), exploring Gemini Pro. Traditional models include Llama-based models (e.g., 70 billion parameters).
- Data Sources: Primarily Snowflake data lake, potentially Azure and AWS S3.
- Financial Background: Good to have experience in financial or regulated industries, or an understanding of finance-related business questions.
- Testing: Basic unit testing and generation of unit test cases are required.
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