VP - AI Engineering

Computer Services, Inc. (CSI) is a forward-thinking software provider helping community and regional financial institutions with open and flexible technologies. The VP - AI Engineering will own the central AI engineering platform and be responsible for delivery velocity, product strategy, developer empowerment, and leadership of a high-performing team.

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
• 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
• 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
• 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
• 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
• 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

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)
• 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)