Amazon.com Services LLC
Technology
AIDataEngineer,RinglinkCustomerServiceEngineeringandInsights
Neural analysis suggests this role is
optimal for Mid+ candidates.
“AI Data Engineer, Ringlink Customer Service Engineering and Insights at Amazon.com Services LLC. Skills: AI Data Engineering, Production systems, Data products. Build AI-Powered Data Products. Build and deploy conversational analytics agents”
What You'll Achieve.
Make business users independent; Ensure answers are trustworthy; Keep AI products healthy; Improve AI products
Industry & Context.
Troubleshoot AI tool issues; Improve AI tool functionality; Address user feedback
What They're Looking For.
Must Have
3+ years of data engineering experience, Experience with data modeling, Experience with warehousing, Experience building ETL pipelines, Experience with SQL
Nice to Have
Experience with AWS technologies, Experience with Redshift, Experience with S3, Experience with AWS Glue, Experience with EMR, Experience with Kinesis, Experience with FireHose, Experience with Lambda, Experience with IAM roles, Experience with permissions, Experience with non-relational databases, Experience with object storage, Experience with document stores, Experience with key-value stores, Experience with graph databases, Experience with column-family databases
What You'll Do.
Build AI-Powered Data Products
Build and deploy conversational analytics agents
Query CS data in natural language
Productionize AI teammates and agents
Answer metrics questions
Wire together full stack
Own end-to-end delivery
Ensure Correctness & Governance
Build validation mechanisms
Define semantic layer
Maintain semantic layer
Own permission architecture
Implement confidence scoring
Implement audit trails
Implement feedback loops
Monitor AI tool performance
Respond to user feedback
Improve what's clunky
Build automated validation
Build alerting for AI outputs
Scale successful patterns
Document what you build
Standardize AI Patterns for Team
Package reusable agents
Package reusable skills
Package prompt templates
Package standard workflows
Contribute to AI development practices
Keep team current on AI tooling
How You'll Work.
Team & Collaboration
Work alongside data engineers; Work alongside platform engineers; Work alongside BI engineers
Full Job Description
We're hiring an AI Data Engineer to build and scale AI-powered analytics tools for Ring & Blink Customer Service. You'll turn AI prototypes into production systems that business users rely on daily — conversational analytics agents, AI teammates, self-service data tools, and intelligent automation. The team has a clear AI vision, active prototypes, and an engineering culture where everyone already uses AI in their daily work. Your job is to take ideas to production, keep them reliable, expand to new use cases, and own what you ship. You'll build fast, ship often, and iterate based on real user feedback. The team uses a mix of AI tools — some running locally for individual productivity, others deployed in the cloud for scalability and broader user access. You'll work across both: building AI agents and tools that range from developer-facing automation to user-facing analytics products. You'll work alongside data engineers, platform engineers, and BI engineers who own the underlying data infrastructure and dashboards. You own the AI-powered layer on top — the part that makes data accessible, answers trustworthy, and users self-sufficient. Key job responsibilities Build AI-Powered Data Products (45%) Business users shouldn't need to file a ticket to get answers. You build the tools that make them independent. * Build and deploy conversational analytics agents that let users query CS data in natural language * Productionize AI teammates and agents for specific use cases — transcript analysis, metrics Q&A, contact summarization, pipeline monitoring — using internal platforms and cloud-hosted agent frameworks * Wire together the full stack: data sources (Redshift, S3) → AI layer (LLMs, agents, semantic logic) → user interface * Own the end-to-end delivery: from prototype handoff through production deployment, user onboarding, and iteration Ensure Correctness & Governance (25%) AI tools that give wrong answers are worse than no tools at all. You make them trustworthy. * Bu
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