Sr. Director, AI-Native Intelligence & Data Insights

Lincoln Financial•Radnor, PA

6d•Hybrid

About The Position

Lincoln Financial Group is building an AI Native Intelligence platform that turns enterprise data — starting with call center audio and transcripts, and expanding to claims, policy, and underwriting data — into a conversational, agentic layer the business can query directly. Instead of static reports, the platform is designed to let business users ask natural-language questions and get grounded, cited answers, backed by a real semantic understanding of Lincoln's data. We're looking for a Senior Director to own the architecture and technical delivery of this platform end to end. This is a hands-on, builder-leader role: someone who sets the technical vision and roadmap, but who is equally comfortable reviewing a Bedrock agent's tool-calling logic, debugging a Step Functions pipeline, or sketching a knowledge graph schema on a whiteboard. You'll build and lead the team that takes this from pilot to enterprise scale.

Requirements

12+ years of experience in data engineering, ML/AI engineering, or applied data science, including 5+ years in a technical leadership role (Director, Senior Manager, or Principal Architect) building production data or AI platforms.
Deep, hands-on experience with AWS data and AI services: S3, Glue, Lambda, Step Functions, Amazon Bedrock (or an equivalent enterprise LLM platform), OpenSearch or another vector database, and a graph database such as Amazon Neptune.
Proven experience building or leading RAG, agentic AI, or conversational AI systems in production — not just proofs of concept.
Strong grasp of data modeling, semantic layers, business ontologies, and knowledge graphs, and how they ground LLM-based systems in accurate, consistent business meaning.
A track record of building, mentoring, and scaling high-performing technical teams.
Excellent communication skills — able to explain architecture trade-offs to engineers and business impact to executives with equal clarity.
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience; advanced degree a plus.

Nice To Haves

Experience with speech-to-text and NLP pipelines (e.g., Amazon Transcribe, Amazon Comprehend), particularly for audio or call center data.
Experience in insurance, financial services, or another regulated industry, with a working understanding of data privacy, PII, and regulatory compliance.
Familiarity with call center analytics, claims processing, or policy administration data.
Experience establishing responsible-AI governance frameworks — bias mitigation, hallucination controls, model evaluation, and audit trails.
Contributions to open-source AI/data tooling, patents, or published technical work.

Responsibilities

Own the end-to-end architecture for the AI Native Intelligence platform on AWS — ingestion, enrichment, the semantic/business ontology layer, retrieval-augmented generation (RAG), knowledge graphs, and agentic orchestration (Amazon Bedrock, Bedrock AgentCore, Amazon Neptune, OpenSearch Serverless, AWS Glue, Amazon Transcribe/Comprehend, and related services).
Translate diverse enterprise datasets — call center transcripts and audio, policy administration, claims, underwriting — into a unified, governed data and semantic foundation that supports natural-language querying and analytics.
Design and evolve the business ontology, knowledge graph, and KPI/metrics glossary that ground the platform's AI in Lincoln's own business definitions, so answers are accurate and consistent with how the business already measures itself.
Lead build-vs-buy decisions across the AI/ML and data stack, and stay ahead of a fast-moving AWS AI landscape — model updates, agent frameworks, vector and graph databases, evaluation tooling.
Build, mentor, and scale a team of engineers, data scientists, and architects; set technical standards, run design/architecture reviews, and unblock hard technical problems.
Partner directly with business stakeholders — call center operations, claims, underwriting, compliance — to identify high-value insight use cases and translate them into technical requirements and delivery plans.
Establish guardrails, governance, and responsible-AI practices: PII handling and redaction, model evaluation, hallucination mitigation, human-in-the-loop review, and auditability.
Define and track success metrics for AI initiatives — retrieval/insight accuracy, adoption, time-to-insight, and cost-to-serve — and report progress to senior and executive leadership.
Stay technically hands-on: prototype new capabilities, review code and architecture, and work directly alongside the team rather than directing from a distance.