AI Implementation Analyst

Corning•Charlotte, NC

1d•$97,314 - $133,808

About The Position

As a Senior AI Implementation Analyst, you will serve as a hands-on AI builder and enablement specialist within the Business Intelligence team's AI Implementation group. You will work closely with market intelligence teammates, competitive intelligence practitioners, data scientists, and business stakeholders to design, build, and deploy practical AI solutions that help domain experts do their jobs better and faster — at scale. This role is focused squarely on implementation: translating ambiguous business intelligence challenges into working AI solutions, not theoretical research. The core problems this team solves — ingesting large volumes of market and competitive intelligence, synthesizing signals across disparate sources, and surfacing conclusions that require reasoning and judgment — are not purely algorithmic. They require AI systems that can reason about complex, nuanced information the way an experienced analyst would. Your job is to build those systems, own them end-to-end, and ensure domain experts can trust and use what you deliver. This role reports directly to the Director of AI Implementation & Forecasting within the Business Intelligence organization.

Requirements

Bachelor's degree in computer science, information systems, data science, engineering, or a related technical field
Demonstrated experience designing and implementing agentic AI workflows, including multi-step reasoning, tool use, and agent orchestration
Strong proficiency in Python, with hands-on experience building AI applications including prompt engineering, retrieval-augmented generation (RAG), embedding pipelines, and API integration with LLM providers
Experience designing and managing knowledge bases, vector databases, or retrieval systems for AI applications, including decisions around corpus curation, chunking, indexing, and retrieval quality
Experience evaluating AI output quality, including building test cases, scoring outputs, identifying failure modes, and iterating on system design based on evaluation results

Nice To Haves

Experience working within a business intelligence, market intelligence, competitive intelligence, or strategy function, or building AI solutions specifically for knowledge-intensive analytical domains
Experience using Databricks for AI application development, data processing, or model serving
Experience with enterprise AI platforms, copilot tools, or centralized AI infrastructure and the ability to navigate both centralized and edge deployment models
Experience applying AI solutions to unstructured text — such as earnings transcripts, research reports, news, regulatory filings, or similar document types
Familiarity with version control, CI/CD practices, or reproducible AI workflow development

Responsibilities

Design, build, and deploy agentic AI solutions — including multi-agent architectures, workflow sequencing, and tool selection — that enable the Business Intelligence team to ingest, synthesize, and reason across large volumes of market intelligence, competitive intelligence, and external data at scale; determine which tasks are best suited to AI versus human judgment.
Architect and manage AI knowledge bases and retrieval systems — including design, chunking strategy, embedding approaches, and ongoing curation — ensuring AI systems have access to the right information and reason from it reliably.
Evaluate and select the most appropriate AI tools, frameworks, and models for each implementation problem; understand the tradeoffs across available options and match the right solution to the specific use case rather than defaulting to a single approach.
Partner closely with market intelligence and competitive intelligence teammates to understand their workflows, translate their domain expertise into AI system requirements, and build solutions that fit naturally into how they work.
Build and maintain structured evaluation and testing frameworks for AI solutions, including test case development, output scoring, failure mode documentation, and quality benchmarks that can be tracked over time.
Support AI solution onboarding and change management for BI team members, ensuring domain experts can effectively use and trust the tools built for them without needing to manage the underlying AI systems themselves.
Stay current with the rapidly evolving AI and large language model landscape and contribute ideas for applying new capabilities where they create practical business value for the intelligence function.