Head of AI Enablement - Truist Care Centers

Truist•Orlando, FL

3d•Onsite

About The Position

The Head of AI Enablement– Truist Care Centers is a senior technology leader responsible for designing and deploying enterprise-grade AI and GenAI solutions across the Truist Care Centers within Truist’s Technology, Data and Operations (TD+O) organization. Reporting directly to the Head of Truist Care Centers, this leader partners with business executives, product managers, and engineering teams to translate business requirements into scalable, production-ready AI applications that drive measurable value. This role is hands-on and technically deep — blending engineering expertise in LLMs, agentic frameworks, and applied ML with the ability to engage directly with business stakeholders. The ideal candidate has built and deployed AI platforms or intelligent products in a technology-first environment, demonstrating fluency in system design, data architecture, and model integration. Acting as both architect and catalyst, the Head of AI Enablement leads cross-functional teams to design and operationalize AI solutions including: • Real-time teammate assist and knowledge retrieval • Conversational AI (voice and chat) • Intelligent call routing and next-best-action • Automated quality assurance and sentiment analytics • Workforce optimization and forecasting augmentation • Post-call summarization and case documentation automation This leader ensures solutions are secure, compliant, ethical, and performant at scale—while establishing reusable engineering patterns that accelerate AI adoption across all Care Center lines of business. The role requires a rare combination of technical acumen, strategic agility, and executive presence. A leader who can earn credibility with engineers and inspire confidence from business Executives. Strategic Impact This role directly influences the Truist Care Centers objectives and key results: • Overall Satisfaction (OSAT) • Service Levels (SL%) • First Call Resolution (FCR) • Average Handle Time (AHT) • Teammate Productivity and Retention • Operational Cost Efficiency (Cost to Serve) • Sales Conversion and Wallet Share (where applicable)

Requirements

Proven experience designing, deploying, and maintaining AI or GenAI systems in production—such as LLM-based solutions, agentic architectures, or advanced ML pipelines.
10+ years leading product, platform, or applied AI engineering teams in high-scale environments (e.g., cloud, SaaS, fintech, or large-scale enterprise systems).
Deep understanding of modern AI infrastructure, including vector databases, model orchestration, RAG pipelines, and MLOps/DevOps integration.
Ability to engage directly with business leaders to convert strategic goals into technical blueprints and deliver working solutions.
Demonstrated knowledge of Responsible AI frameworks, model risk governance, and secure data management practices in regulated industries.
Ability to earn trust across senior leadership, from CIOs to product executives, by articulating AI’s value in business terms.
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field (advanced degrees preferred) or equivalent experience.

Nice To Haves

Proven experience designing, deploying, and operating AI or GenAI systems in production environments supporting customer-facing or service operations (e.g., conversational AI, teammate assist, LLM-based knowledge retrieval, ML-based routing).
10+ years leading product, platform, or applied AI engineering teams in large-scale, high-availability environments.
Demonstrated understanding of contact center ecosystems, including telephony platforms (i.e., AWS, Genesys, NICE, etc.), CRM integration, QA workflows, and workforce management systems.
Deep knowledge of AI infrastructure including vector databases, RAG pipelines, model orchestration, streaming APIs, low-latency deployment patterns, and MLOps integration.
Ability to convert frontline performance challenges (OSAT, SL%, FCR, AHT, abandonment rates) into technical blueprints and measurable AI use cases.
Experience implementing AI solutions within regulated, customer-sensitive environments with strong data privacy and model governance controls.
Ability to build credibility with engineers while influencing Contact Center executives and operations leaders.
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (advanced degree preferred) or equivalent experience.

Responsibilities

Translate complex business requirements into engineered AI and GenAI solutions that deliver measurable business outcomes.
Architect and lead the development of LLM-based, agentic, and machine-learning systems that integrate with enterprise data and technology platforms.
Guide engineering teams in model development, fine-tuning, and deployment, ensuring performance, security, and compliance.
Build reusable frameworks, APIs, and components to accelerate AI adoption across product lines.
Partner directly with the TD&O Divisional Leaders and their engineering, product, and operations teams to identify high-impact use cases and embed AI capabilities into existing workflows.
Serve as a trusted engineering partner to business executives, translating strategic goals into technical blueprints.
Foster a builder culture rooted in experimentation, delivery, and responsible innovation.
Establish and enforce AI engineering standards—including model observability, version control, and performance telemetry in partnership with Enterprise Architecture and the Policy, Standards, Practices Governance team.
Stay ahead of emerging AI technologies, tools, and frameworks; continuously assess opportunities to integrate frontier capabilities.