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Lincoln Financial

Insurance, retirement, and financial services provider

Sr. Director - AI Native Analytics

AI Application EngineeringUpdated on Nov 14
Full-time

About the Job

Requirements

  • At least 8 years of experience in Data Analytics or Business Intelligence, including at least 2 years building production LLM-based analytics systems.
  • Hands-on, production-level expertise with Amazon Bedrock (Knowledge Bases and Agents/AgentCore), AWS Lambda, Step Functions, Amazon S3, AWS Glue, Amazon Athena, and a vector store such as OpenSearch Serverless.
  • Practical prompt-engineering and LLM application development skills, including structured extraction, tool-calling or agentic patterns, retrieval design, and iterative evaluation against real questions.
  • Understanding of semantic layers, business ontologies, or metrics layers, and grounding LLM outputs in governed business definitions rather than ad hoc computation.
  • Experience building or applying pattern-, trend-, or anomaly-detection techniques over enterprise data and making findings actionable for business users.
  • Proficiency in Python and/or TypeScript/Node.js for extraction pipelines, agent tools, and integration code.
  • Working knowledge of SQL and cloud analytics engines such as Athena or Redshift to design and validate natural-language-to-SQL experiences.
  • Ability to demonstrate working systems to engineers and explain their behavior and limitations to non-technical business stakeholders.
  • A bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Responsibilities

  • Design and build the conversational AI experience end to end using Bedrock Agents (via AgentCore or MCP/API), retrieval-augmented generation over Bedrock Knowledge Bases or OpenSearch Serverless, natural-language-to-SQL over Athena, and relationship queries over a knowledge graph such as Amazon Neptune or GraphRAG.
  • Build capabilities to detect patterns, trends, and anomalies across enterprise data and proactively surface notable findings.
  • Build and tune the AI extraction pipeline to turn unstructured documents, logs, notes, and communications into structured, query-ready facts using Amazon Comprehend and/or LLM-based extraction via the Bedrock Converse API, Lambda, Step Functions, and DynamoDB for state.
  • Own prompt engineering and agent tool design, including system instructions, tool/action-group definitions, structured-output schemas, and model selection and tiering to balance accuracy, latency, and cost.
  • Integrate the conversational layer with the platform's semantic layer and business ontology, including KPI and metrics definitions and the business rules glossary, to keep agent answers consistent with governed business measures.
  • Build evaluation and quality processes for LLM-based systems, including golden question sets, retrieval and accuracy scoring, hallucination and abstention testing, and regression checks before changes ship to users.
  • Implement guardrails with security and compliance partners, including PII detection and redaction, Bedrock Guardrails configuration, access-controlled data zones, and audit logging for AI-generated answers.
  • Partner with enterprise business stakeholders to translate questions into conversational and pattern-detection features and iterate using usage and accuracy feedback.
  • Mentor and provide technical direction to engineers contributing to the conversational AI workstream and represent the workstream in architecture reviews led by the Senior Director.
  • Monitor AWS AI developments, including Bedrock model and AgentCore updates and retrieval and evaluation tooling, and provide concrete recommendations to the team.

Desired Qualifications

  • Experience with GraphRAG or other knowledge-graph-grounded retrieval patterns and a graph database such as Amazon Neptune.
  • Experience with a semantic-layer or metrics-layer tool such as Cube.dev to serve consistent metrics to BI and AI consumers.
  • Experience with NLP/document-processing pipelines such as Amazon Comprehend for extracting structure from varied unstructured enterprise data.
  • Experience in insurance, financial services, or another regulated industry, with an understanding of PII handling and data privacy and compliance considerations.
  • Experience configuring LLM safety and evaluation tooling such as Bedrock Guardrails, or building an evaluation harness for generative AI systems.
  • Familiarity with enterprise data domains such as financial, operational, customer, or workforce data within a large organization.

About the Company

Lincoln Financial

Insurance, retirement, and financial services provider

Lincoln Financial Group offers life insurance, annuities, and retirement income products through its network of affiliated companies, and also provides securities and investment advisory services via affiliated broker-dealers. Its products combine insurance coverage or retirement-focused contracts with investment options, where customers fund premiums, choose subaccounts or riders, and receive benefits such as death benefits, living benefits, or guaranteed income in retirement. The company differentiates itself with a family of affiliated brands and distributors that coordinate life, annuity, and advisory products under one umbrella. Its goal is to help individuals and families protect loved ones, plan for retirement, and achieve financial security through integrated protection and investment solutions.

Company size
Not listed
Company stage
Not listed
Total funding
$128.8M
Headquarters
Radnor Township, Pennsylvania
Founded
1905

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