Data Analytics AI Engineer - Fireblocks

Design and build LLM-powered systems that directly improve product capabilities and customer experience - enabling Product, Engineering, and Business teams to make faster, smarter, data-driven decisions at scale Evaluate and quantify the business impact of AI initiatives through rigorous experimentation, benchmarking, and model evaluation to continuously optimize LLM performance Continuously monitor, refine, and evolve AI models and solutions - iterating on prompt engineering and deployment practices to keep pace with rapidly advancing capabilities and shifting business needs. Leverage AI-assisted development tools (e.g., Cursor, Claude Code) to accelerate delivery speed and engineering velocity across the team Analyze large-scale datasets to surface strategic insights, define and track critical KPIs, and translate analytical findings into actionable product and business recommendations. Strong analytical background - experience defining KPIs and communicating data-driven recommendations to stakeholders. 5+ years of experience in AI/ML engineering or a combined data analytics and AI role Hands-on experience with LLMs, prompt engineering, fine-tuning, and model evaluation pipelines. Proficiency in Python and SQL; experience building and deploying production-grade AI applications. Practical knowledge of MCP, database agents, and semantic views/YAMLs - Snowflake as a database agent platform - advantage. Strong cross-functional communication skills - able to present AI concepts and outcomes to both technical and non-technical audiences. Self-motivated and collaborative, with the ability to operate independently within a global team. Familiarity with the digital assets, fintech, or Web3 domain - advantage

Design and build LLM-powered systems that directly improve product capabilities and customer experience - enabling Product, Engineering, and Business teams to make faster, smarter, data-driven decisions at scale Evaluate and quantify the business impact of AI initiatives through rigorous experimentation, benchmarking, and model evaluation to continuously optimize LLM performance Continuously monitor, refine, and evolve AI models and solutions - iterating on prompt engineering and deployment practices to keep pace with rapidly advancing capabilities and shifting business needs. Leverage AI-assisted development tools (e.g., Cursor, Claude Code) to accelerate delivery speed and engineering velocity across the team Analyze large-scale datasets to surface strategic insights, define and track critical KPIs, and translate analytical findings into actionable product and business recommendations.

Design and build LLM-powered systems that directly improve product capabilities and customer experience - enabling Product, Engineering, and Business teams to make faster, smarter, data-driven decisions at scale Evaluate and quantify the business impact of AI initiatives through rigorous experimentation, benchmarking, and model evaluation to continuously optimize LLM performance Continuously monitor, refine, and evolve AI models and solutions - iterating on prompt engineering and deployment practices to keep pace with rapidly advancing capabilities and shifting business needs. Leverage AI-assisted development tools (e.g., Cursor, Claude Code) to accelerate delivery speed and engineering velocity across the team Analyze large-scale datasets to surface strategic insights, define and track critical KPIs, and translate analytical findings into actionable product and business recommendations. Strong analytical background - experience defining KPIs and communicating data-driven recommendations to stakeholders. 5+ years of experience in AI/ML engineering or a combined data analytics and AI role Hands-on experience with LLMs, prompt engineering, fine-tuning, and model evaluation pipelines. Proficiency in Python and SQL; experience building and deploying production-grade AI applications. Practical knowledge of MCP, database agents, and semantic views/YAMLs - Snowflake as a database agent platform - advantage. Strong cross-functional communication skills - able to present AI concepts and outcomes to both technical and non-technical audiences. Self-motivated and collaborative, with the ability to operate independently within a global team. Familiarity with the digital assets, fintech, or Web3 domain - advantage