VP - AI Engineering
About the role
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.
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.
Partner with the Product Operations and other teams to keep practices current and capture institutional knowledge before it is lost.
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.
PREFERRED: Explicit build-vs-buy judgment across the model-platform landscape (Amazon Bedrock, Azure AI Foundry, Anthropic Claude, Microsoft Copilot, Atlassian Rovo).
PREFERRED: Experience standing up MCP-based connector and agent ecosystems.
PREFERRED: Familiarity with the community-bank and credit-union market and the competitive set (Fiserv, FIS, Jack Henry, Q2, Alkami).
Qualifications
Master's degree in Computer Science, Engineering, or related field.
Proven ability to lead and mentor a team of engineers.
Strong understanding of cloud-native architectures and tools.
Experience with CI/CD pipelines and automation tools.
Excellent communication and interpersonal skills.
Skills
Leadership and strategic thinking.
Technical expertise in AI/ML platforms and tools.
Experience with AI/ML model lifecycle management.
Ability to manage and optimize resource usage.
Experience with agile methodologies and DevOps principles.
Benefits
CSI provides rewarding and challenging career opportunities for our employees. When determining your pay, we consider various factors such as your skills, qualifications, experience and location. Along with a competitive salary, this position includes eligibility for incentive awards based on both individual and business performance. We also offer a comprehensive range of benefits.
Pay
Competitive salary based on experience and qualifications.
Schedule
Full-time position.
Contact
To learn more about our benefits, visit: Benefits Summary
Equal Opportunity Employer
CSS is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, physical and mental disability, marital status, veteran status, or any other characteristic protected by applicable law. If you need an accommodation during the recruitment process, please email us at recruiter@csiweb.com and we will work with you to meet your accessibility needs.
Visa Sponsorship
We are unable to offer visa sponsorship for this position. Applicants must be authorized to work in the United States without the need for sponsorship now or in the future.