Staff Technical Program Manager - AI Enablement

Candescent•Atlanta, GA

About The Position

Candescent is a technology company focused on delivering intelligent banking experiences through digital banking, account opening, and branch solutions. Its approach combines human-centered design with data, automation, and cloud-based innovation, supported by an API-first architecture. The Staff Technical Program Manager - AI Enablement leads enterprise-wide evaluation, rollout, adoption, and operationalization of artificial intelligence capabilities. The role translates AI strategy into executable technical programs and serves as an execution bridge among engineering, platform teams, IT, security, privacy, legal, procurement, risk, and business stakeholders. It guides initiatives from intake and proof of concept through production readiness and scaled adoption, aligning delivery plans, governance controls, vendor decisions, and measurable outcomes with business objectives, architectural standards, and regulated-environment expectations. The role also establishes and maintains AI enablement processes, responsible AI practices, operational models, adoption plans, and measurable performance indicators. It coordinates technical and business workstreams, communicates program status and risks to executive leadership, and advances consistent, scalable program-management practices across the AI portfolio.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field, or equivalent practical experience.
At least 8 years of experience leading complex technical programs in software, cloud, data, platform, or AI-adjacent environments.
Proven track record managing large-scale, cross-functional programs spanning multiple teams, technologies, and stakeholder groups.
Strong technical fluency and ability to engage credibly with engineering, platform, architecture, security, privacy, and operations teams.
Strong understanding of the software development lifecycle, Agile delivery, DevOps practices, production readiness, and change management.
Experience defining roadmaps, success metrics, integrated plans, dependency models, risk controls, and executive-level program reporting.
Demonstrated ability to operate effectively in regulated, high-risk, or control-oriented environments.
Excellent written and verbal communication, executive presence, and stakeholder-management skills.
Ability to adapt strategy and execution in fast-moving, ambiguous environments while maintaining clear accountability and governance.

Nice To Haves

Experience leading enterprise AI enablement, generative AI, machine learning platform, data, cloud, or developer-productivity programs.
Working knowledge of responsible AI practices and AI governance frameworks, including NIST AI RMF and ISO/IEC 42001.
Experience evaluating and onboarding third-party technology vendors, including security, privacy, data handling, licensing, access, architecture, and operational-readiness considerations.
Familiarity with Google Cloud, Microsoft Azure, or AWS; modern platform architectures; APIs; and enterprise integration patterns.
Experience defining adoption strategies, enablement programs, training coordination, and outcome-based metrics for new technology capabilities.
PMP, PgMP, Scrum, SAFe, or comparable program-management certification.
AI governance, security, privacy, or risk certification such as AIGP, CISM, CDPSE, or an equivalent credential.

Responsibilities

Own the end-to-end lifecycle of complex AI enablement programs, from intake, discovery, and proof of concept through production rollout, adoption, and ongoing operationalization.
Translate enterprise AI strategy into roadmaps, milestones, program plans, dependencies, success measures, and delivery commitments.
Define program scope, goals, technical and operational readiness criteria, and measurable outcomes with engineering and business stakeholders.
Prioritize concurrent AI initiatives based on business value, technical readiness, risk, capacity, and organizational impact.
Maintain integrated schedules, risk assessments, issue logs, decision records, and mitigation plans for high-impact programs.
Coordinate engineering, platform, IT, security, privacy, legal, procurement, risk, and business stakeholders.
Identify cross-team dependencies, resolve blockers, clarify ownership, and drive decisions and delivery across workstreams.
Align technical implementation, platform integration, deployment strategy, and operational readiness with business objectives.
Communicate program status, outcomes, risks, trade-offs, and decisions to executive leadership and key stakeholders.
Provide program-level guidance on architecture, integration, release planning, deployment, and support readiness while preserving engineering ownership.
Embed responsible AI practices into AI evaluation, implementation, rollout, and ongoing enablement.
Partner with Risk, Privacy, Security, and Legal teams to align initiatives with internal standards and applicable regulatory expectations.
Coordinate alignment of AI tools and operating practices with frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001.
Maintain audit-ready program documentation, control evidence, risk decisions, approvals, and traceability.
Incorporate governance requirements into delivery plans early enough to support speed, accountability, and production readiness.
Lead structured evaluation, onboarding, and operationalization of third-party AI tools, platforms, and services.
Coordinate technical, security, privacy, data-handling, procurement, licensing, access-control, and architectural-fit assessments.
Define pilot objectives and evaluation criteria, synthesize findings, and recommend broader adoption, remediation, or exit.
Coordinate production-readiness activities, support models, ownership transitions, and rollout dependencies for approved AI capabilities.
Define and maintain the AI enablement operating model, including intake processes, evaluation workflows, decision gates, ownership, and escalation paths.
Drive adoption through enablement planning, stakeholder engagement, training coordination, reusable guidance, and transparent communications.
Establish and report KPIs for AI usage, adoption, value realization, delivery health, and risk posture.
Introduce scalable program-management practices, templates, tools, and continuous-improvement mechanisms across the AI portfolio.
Promote accountability, transparency, responsible experimentation, and measurable innovation.