Dir Software Engineering - AI Assisted Services

Citizens Bank

8h•Hybrid

About The Position

The Director of Engineering for Assisted Services will lead the engineering strategy, architecture, and delivery of platforms that power human-assisted customer interactions across the bank—including contact center tooling, advisor/agent workflows, servicing orchestration, customer communication flows, and intelligent automation. This leader will unify fragmented servicing platforms, modernize legacy components, and accelerate our transition to an event‑driven, API‑first, and increasingly AI‑assisted servicing ecosystem. The Director will oversee engineering teams building next‑generation capabilities such as AI‑guided agent experiences, intelligent case routing, natural‑language search, automated summarization, and contextual recommendations—all designed to elevate both the colleague and customer experience. The role requires deep engineering leadership, strong architectural discipline, operational excellence, and hands-on experience designing or integrating AI‑powered applications into complex servicing environments.

Requirements

12+ years of hands‑on software engineering experience building large‑scale, customer-facing systems.
10+ years leading engineering teams in complex, multi‑platform environments.
Proven experience delivering or integrating AI‑powered applications, such as agent‑assist tools, conversational AI, ML-driven analytics, or intelligent process automation.
Expertise in modern engineering practices, including Agile/Scrum, DevOps, CI/CD, release management, and API-led development.
Strong proficiency in multiple languages (e.g., Java, Python, JavaScript/React, Go, C#) and experience with cloud platforms (AWS/Azure/GCP).
Deep knowledge of distributed systems, event‑driven design, and high‑availability architectures.
Excellent communication skills, capable of influencing senior stakeholders across business and technology.
Bachelor’s degree in Computer Science, Engineering, or similar technical discipline.

Nice To Haves

Experience in financial services, especially in servicing platforms, contact centers, CRM, case management, identity, or authentication.
Background deploying or scaling AI/ML solutions (NLP, classification models, vector search, LLM‑based applications).
Familiarity with responsible‑AI frameworks, model governance, and regulatory considerations in a financial services context.
Ability to navigate complex organizational structures and drive alignment across multiple senior leaders.
Master’s degree in Software Engineering, Computer Science, AI/ML, or related field.

Responsibilities

Lead, inspire, and develop high‑performing engineering teams, fostering a culture of innovation, ownership, and engineering excellence.
Define the technical strategy for Assisted Services across servicing platforms, omni‑channel agent tools, workflow engines, and customer interaction systems.
Drive the adoption of AI‑assisted servicing capabilities, including conversational AI, machine learning–based recommendations, agent assist tooling, and automated knowledge retrieval.
Shape and implement an architectural vision that unifies assisted and self‑service experiences into a cohesive, modern servicing ecosystem.
Partner with Product, Operations, Risk, Enterprise Architecture, and Data Science to ensure AI solutions are aligned with customer needs, responsible‑AI standards, and regulatory requirements.
Oversee delivery of core servicing capabilities including call center tooling, unified agent desktops, case management, workflow automation, and system integrations.
Lead engineering efforts to build and deploy AI-driven enhancements such as: Real‑time agent assist (summaries, recommendations, prompts) Predictive servicing and intelligent routing Contextual data retrieval and knowledge search NLP‑powered insights to shorten handle times and improve accuracy
Ensure teams have clear requirements, technical specifications, and a strong delivery operating model to meet timelines and quality expectations.
Implement robust engineering processes, tooling, and CI/CD pipelines that support rapid iteration and frequent releases.
Embed secure-by-design, privacy, and responsible‑AI practices across all servicing applications.
Ensure AI models and features adhere to ethical, compliance, and regulatory guidelines—including transparency, explainability, and model‑risk controls.
Strengthen platform reliability and performance through observability tooling, automated quality checks, and proactive monitoring.
Collaborate with Data Science and AI teams to operationalize models into production systems, including inference pipelines, model monitoring, and lifecycle management.
Partner with Operations to understand agent workflows, reduce friction, and identify where AI can drive meaningful improvements in efficiency and consistency.
Engage with Fraud, Identity, Security, and Compliance teams to ensure AI‑assisted solutions uphold trust and customer protection.