Carefull - Data Scientist / AI Engineer

silver; United States; Remote; USD 170,000 - 260,000; Full time

Job summary

silver is seeking a senior AI engineer to join the Data team and build detection systems for a financial safety product focused on protecting older-adult customers. You will explore fraud patterns, create production-grade detection logic, and develop data enrichment pipelines, with rigorous evaluation of model and pipeline performance.

The role requires strong Python skills, hands-on LLM experience, cloud deployment, and a bias toward measurable outcomes through error analysis and end-to-end

Qualifications

• Strong Python skills with experience building data pipelines and production systems.
• Hands-on experience building LLM applications in production, including prompting, structured outputs, context management, and working with provider SDKs and APIs.
• Comfort deploying and operating what you build on a cloud platform.
• A measurement-first habit, with the ability to set up evaluations, read precision and recall, and perform error analysis.
• Track record of owning work end to end with minimal supervision.
• Genuine curiosity about how money moves through the US financial system and how scammers exploit it.
• Comfort reasoning about ambiguity in a domain where answers often depend on context.
• Clear written and verbal communication in English.

Responsibilities

• Design and ship AI-driven detection features from initial prototype through production deployment
• Build data enrichment pipelines that extract structured information from messy, real-world financial transaction data
• Research fraud and scam typologies relevant to older adults and translate findings into scalable detection logic
• Build reproducible evaluations including test sets, metrics, and error analysis to measure every change
• Investigate production or user-reported issues, identify root causes, and ship fixes
• Optimize AI pipelines for accuracy, latency, and cost, making informed tradeoffs about model selection and architecture
• Partner with customer care, go-to-market, and partner-facing teams to understand real user needs
• Track new developments in LLMs and agents and find practical ways to apply them to the product

Skills

Python

Data pipelines

LLM apps

Cloud deployment

Fraud research

End-to-end ownership

English communication

Job description

Role overview

A senior AI engineering role on the Data team building the detection systems at the heart of a financial safety product focused on protecting older-adult customers. The work spans research into fraud patterns, production-grade detection logic, data enrichment pipelines, and rigorous evaluation of model and pipeline performance.

Responsibilities

• Design and ship AI-driven detection features from initial prototype through production deployment
• Build data enrichment pipelines that extract structured information from messy, real-world financial transaction data
• Research fraud and scam typologies relevant to older adults and translate findings into scalable detection logic
• Build reproducible evaluations including test sets, metrics, and error analysis to measure every change
• Investigate production or user-reported issues, identify root causes, and ship fixes
• Optimize AI pipelines for accuracy, latency, and cost, making informed tradeoffs about model selection and architecture
• Partner with customer care, go-to-market, and partner-facing teams to understand real user needs
• Track new developments in LLMs and agents and find practical ways to apply them to the product

Requirements

• Strong Python skills with experience building data pipelines and production systems
• Hands-on experience building LLM applications in production, including prompting, structured outputs, context management, and working with provider SDKs and APIs
• Comfort deploying and operating what you build on a cloud platform
• A measurement-first habit, with the ability to set up evaluations, read precision and recall, and perform error analysis
• Track record of owning work end to end with minimal supervision
• Genuine curiosity about how money moves through the US financial system and how scammers exploit it
• Comfort reasoning about ambiguity in a domain where answers often depend on context
• Clear written and verbal communication in English

Nice to have

• AWS experience with Lambda, CDK, Bedrock, Redshift, or DynamoDB
• Familiarity with LLM observability and tracing tools such as Langfuse or LangSmith
• Background in fraud detection, fintech, or risk and compliance
• Experience with financial transaction data including ACH, Zelle, wires, or card payments
• Experience working with regulated institutions such as banks
• Exposure to elder care, aging-in-place, or financial vulnerability research
• Background in data science or ML beyond LLMs, including statistical modeling or anomaly detection