Enterprise AI Engineer

Customers Bank•Malvern, PA

About The Position

The Enterprise AI Engineer is a high-impact, highly visible role at the intersection of artificial intelligence, business strategy, and hands-on technical execution. This is a field-facing, mission-critical role for engineers who think like founders and operate like consultants. In this role, you won’t just be building technology for the bank; you’ll be working side by side with engineers from our strategic partners, such as OpenAI & ElevenLabs. The Enterprise AI Engineer will be a member of a central team fully focused on delivering automation & AI-powered solutions that solve real problems and create measurable impact. You will partner with business units and functional areas to understand complex challenges, design and build production-grade AI and automation workflows, and implement scalable systems and elevated capability. This role demands a rare combination: deep technical acuity, executive presence, and the instinct to translate ambiguous business needs into working solutions.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, Data Science, or a related technical field; advanced degree a plus.
Minimum 4+ years of experience in software engineering, AI/ML engineering, technical deployment, or a related field, including demonstrated customer- or business-facing delivery.
Proven ability to build and ship production-grade AI systems using LLMs / generative AI
Strong full-stack engineering proficiency with Python, JavaScript/TypeScript, or comparable languages.
Demonstrated ability to scope complex technical projects, drive delivery in ambiguous environments, and manage competing priorities without losing momentum.
Outstanding communication skills: able to engage C-suite stakeholders and technical engineers with equal fluency.
High degree of intellectual curiosity and a growth mindset — you actively seek out what you don’t know and close those gaps fast.
Ability and willingness to travel to business unit sites across the institution and to key external engagements as needed.
Proficiency in Python and JavaScript/TypeScript for full-stack AI development.
Experience with LLM orchestration frameworks and API-based model deployment.
Familiarity with cloud platforms (AWS, Azure, or GCP)
Proficiency in Microsoft Office applications (Word, Excel, PowerPoint, Outlook) for executive communication and documentation.
Experience with project management and collaboration tools such as Jira, Confluence, SharePoint, or similar platforms.

Nice To Haves

Experience working in regulated industries — financial services, healthcare, or government — with an understanding of compliance and risk considerations in AI deployment.
Familiarity with banking operations, risk management, BSA/AML processes, or financial crimes compliance functions.
Background in data engineering or platform engineering in cloud environments (AWS, Azure, or GCP).
Experience working in a forward-deployed, consulting, or embedded engineering capacity with business clients.
Exposure to enterprise AI governance frameworks, model risk management, or responsible AI practices.
Professional certifications in AI/ML, cloud architecture, or project management are a plus.

Responsibilities

Embed within business functions across the institution to identify, scope, and deliver automation and AI-enabled solutions that address high-priority operational and strategic challenges.
Translate complex, ambiguous business problems into structured technical requirements and delivery plans.
Lead end-to-end project delivery from discovery and prototyping through production deployment and adoption, maintaining clear accountability at each stage.
Build production-ready solutions with strong engineering fundamentals: reliability, observability, security, and scalability.
Write, review, and ship code across the stack using Python, JavaScript/TypeScript, or comparable languages.
Integrate AI capabilities with core banking platforms, data infrastructure, and third-party systems via APIs and data pipelines.
Ensure AI systems meet model risk management standards, data governance policies, and applicable regulatory expectations.
Develop playbooks, reusable components, and implementation patterns that accelerate future AI deployments across the institution.
Represent AI capabilities and implementation insights in executive and governance forums as needed.
Build trusted relationships with senior business leaders, risk officers, operations leaders, and technology teams across the institution.
Communicate complex technical concepts clearly and compellingly to non-technical audiences, adapting messaging to the audience.
Lead change management and adoption efforts to ensure AI solutions are embedded, understood, and sustained within business teams.
Facilitate working sessions, stakeholder reviews, and demos that drive alignment and accelerate decision-making.
Ensure deployed AI systems are developed and maintained in accordance with the Bank’s AI governance framework, model risk management standards, and applicable regulatory guidance (OCC, FFIEC, FinCEN).
Coordinate with Model Risk Management, Compliance, Legal, and Audit teams to support reviews, validations, and documentation of AI systems.
Maintain accurate records for AI model inventories, governance logs, and deployment documentation.
Monitor and communicate emerging regulatory developments related to AI/ML use in financial services.
Identify risks in AI systems early and surface them through appropriate governance channels with recommended mitigations.