Senior AI Analytics Engineer

Senior AI Analytics Engineer — Natural Language and Contact Center Analytics

Position Overview

Our client is seeking a hands‑on engineer to build an analytics tool that lets business users ask questions in natural language and receive reliable answers from structured business data and unstructured call center data.

The successful candidate will have built a working analytics application—not just a chatbot demonstration—and will understand how to connect call transcripts with operational records, produce accurate answers, and show users the information behind those answers. The solution will be built on AWS, with Amazon Bedrock and Amazon SageMaker in the technology stack.

Responsibilities

• Design and build a natural language interface for questions about call volume, customer issues, repeat contacts, agent activity, and other business measures.
• Connect unstructured information, such as call transcripts and notes, with structured information, such as call metadata and operational records.
• Use Amazon Bedrock to develop language model and retrieval workflows that find relevant information and generate supported answers.
• Build data pipelines and query workflows that retrieve and analyze the right records for each question.
• Use Amazon SageMaker where custom machine learning models, model deployment, or monitoring are needed.
• Test the accuracy of retrieved information, generated queries, calculations, and final answers.
• Implement appropriate source traceability, access controls, and handling of sensitive customer information.
• Work with business and engineering partners to deploy and improve the tool for production use.

Required Qualifications

• Hands‑on experience designing and building a natural language analytics or conversational business intelligence application.
• Experience combining structured data with unstructured text to answer business questions.
• Experience with call center, contact center, or comparable customer interaction data, including transcripts or conversation records.
• Practical experience building AI applications on AWS and Amazon Bedrock.
• Strong Python and SQL skills, including experience with data pipelines, APIs, and querying business data.
• Experience with retrieval‑augmented generation (RAG), semantic search, or a comparable method for finding relevant content in large collections of text.
• Ability to describe a system they personally built, how it was deployed, and how they measured answer accuracy.

Preferred Qualifications

• Experience with Amazon SageMaker for custom model development, deployment, or monitoring.
• Experience with AWS data and search services such as Amazon S3, AWS Glue, Amazon Athena, Amazon OpenSearch Service, or comparable tools.
• Experience with text‑to‑SQL, embeddings, vector search, and evaluation of language model responses.
• Experience with call transcription or conversation analytics tools, such as Amazon Transcribe Call Analytics or an enterprise contact center platform.
• Experience building analytics tools for financial services or another environment with sensitive customer data.