VP - AI Engineering

CSI•Remote - Tennessee, TN

About The Position

The VP - AI Engineering owns the central AI engineering platform — the “AI Harness” — together with the agent frameworks, evaluation infrastructure, and AI-assisted development practices that every CSI engineering team builds on. A small central team builds the platforms, connectors, and plumbing across LLMs, data, knowledge, and workflows; functional teams build on top of them. This role is accountable for a hard delivery-velocity number, for the engineering bar on AI- and agent-generated code, and for the developer enablement that makes adoption real rather than nominal.

Requirements

10+ years of experience in senior leadership of an AI/ML platform, applied-AI, or engineering-productivity organization at meaningful scale, with accountability for delivered outcomes — not research output alone.
A track record of raising software delivery velocity (lead time, deployment frequency, change-failure rate) through platform and developer-experience investment.
Deep, current fluency in LLM application engineering — agent frameworks, RAG, evals / LLMOps, prompt and model lifecycle, and cost / latency management at production scale.
Hands-on experience operationalizing AI-assisted and agentic software development with the quality and security guardrails that make it safe.
Experience operating to bank-grade security, compliance, and audit requirements (financial services, fintech, or comparably regulated).
Applicants must be authorized to work in the United States without the need for sponsorship now or in the future.

Nice To Haves

Preferred experience in building an internal AI agent platform consumed by multiple product teams — with capability boundaries, permissioning, state / memory across sessions, and rollback for agent-caused failures.
Explicit build-vs-buy judgment across the model-platform landscape (Amazon Bedrock, Azure AI Foundry, Anthropic Claude, Microsoft Copilot, Atlassian Rovo).
Experience standing up MCP-based connector and agent ecosystems.
Familiarity with the community-bank and credit-union market and the competitive set (Fiserv, FIS, Jack Henry, Q2, Alkami).

Responsibilities

Own the delivery-velocity mandate: Set and hit the ~50% delivery-acceleration target; establish the Year-0 baseline and the DORA-style metrics (lead time for change, deployment frequency, change-failure rate, time to restore), and report progress to the CDAO and pillar peers. Translate individual adoption into organizational throughput — redesign team scope and workflow around human judgment plus agent execution, not individual task completion.
Build the AI Harness as a product: Own the strategy and roadmap for the central AI platform — model routing, connectors, MCP servers, and the build-vs-buy framework that decides when to use Bedrock or Foundry rather than direct LLM access. Treat the Harness as a product, not infrastructure — named services, SLAs, adoption metrics, and a backlog driven by engineering-team needs — while managing token and cost optimization across LLM usage.
Empower developers and agents: Build and maintain the agent framework and the shared library of skills, connectors, and agents; set the standards for agent and connector architecture so teams compose rather than rebuild. Establish the target SDLC and common AI code / delivery tooling; define golden paths and the guardrails, gates, and review patterns that make AI-assisted and agentic development safe at bank-grade standards.
Evaluation, quality & security: Own eval and LLMOps infrastructure — versioning, regression and faithfulness testing — as the gate for shipping AI capabilities. Embed AI security, data-classification enforcement, audit / logging, and use-case risk review at the platform layer, in close partnership with Information Security and governance.
Enablement & adoption: Own the tooling-access strategy and the enablement engine — onboarding, office hours, and change management — so adoption is measured and real, not nominal. The unlock is a real result on day one, not training. Partner with the Product Operations and other teams to keep practices current and capture institutional knowledge before it is lost.
Organization & leadership: Build, develop, and lead a high‑performing organization of delivery and operational leaders, fostering accountability, continuous learning, and readiness for new products and enhancements. Partner cross‑functionally to align with company strategy, product evolution, and enterprise standards.