New opportunity

Agentic AI Delivery Leader

Ascensus · Charlotte, United States

About this role

Ascensus seeks an Agentic AI Delivery Leader within its Transformation team to lead an enterprise portfolio of AI agent initiatives from opportunity discovery and business case through design, deployment, adoption, and measured outcomes. The role is based in Charlotte, North Carolina, with a hybrid working arrangement.

Responsibilities

Own a multi-quarter roadmap of agentic AI use cases, prioritizing business value, scalability, quality, and measurable results. Assess user journeys and data and system constraints; frame solutions involving agent tools and actions, guardrails, and autonomous or human-in-the-loop workflows.
Develop business cases covering costs, revenue, and customer experience; secure executive alignment and funding; establish baselines and measure benefits through adoption and continuous improvement.
Coordinate product, AI engineering, data science, architecture, operations, and external partners to deliver pilots and scaled deployments. Manage scope, dependencies, risks, delivery quality, executive steering, and program governance using Agile or hybrid approaches.
Work with the Responsible AI leader on data-handling controls, model and vendor risk, explainability, operational resilience, content filtering, and escalation paths. Lead launch planning, training, communications, hypercare, feedback, and post-launch improvements.
Advise executives on operating-model changes and investment decisions; lead governance forums, oversee vendor and platform decisions, and supervise an Agentic AI Delivery Principal. Protect confidential client and company data.

Requirements

Bachelor’s degree and 8+ years in digital transformation, product or program management, or consulting; 4+ years delivering AI/ML and generative AI solutions in production; and 1+ year delivering agentic AI solutions in production.
Experience delivering LLM-powered copilots or agents through discovery, evaluation, deployment, and iteration. Understand RAG, tool or function calling through MCP or APIs, autonomous AI, human-in-the-loop approaches, and prompt and configuration management.
Ability to lead concurrent workstreams, establish governance and delivery metrics, communicate with senior stakeholders, shape backlogs and acceptance criteria with technical teams, and support testing and release readiness. Strong analytical, communication, leadership, mentoring, and benefits-tracking skills are required.

Preferred

An MBA or equivalent advanced degree. Ascensus does not provide visa sponsorship now or in the future and does not hire candidates working under OPT.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Agentic AIAI agentsLarge language modelsGenerative AIMachine learningRAGMCPAPIsFunction callingHuman-in-the-loop systemsPrompt managementAI evaluationResponsible AIAgile deliveryProgram managementPortfolio managementProduct managementStakeholder managementChange managementVendor managementBenefits measurementExecutive communication

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Shape the enterprise portfolio
Own a multi-quarter roadmap of agentic AI use cases. The posting calls for discovery across user journeys and data and system constraints, with trade-offs based on business value, scalability, quality and portability.
Secure funding and prove value
Build and defend business cases covering cost, revenue and customer experience; align executives on investment; establish baselines and attribution; and track benefits through adoption and improvement.
Deliver production-ready agents
Translate opportunities into tools and actions, guardrails, and autonomous or human-in-the-loop workflows. Partner with the Responsible AI leader on data handling, model and vendor risk, explainability, resilience and escalation paths.
Lead delivery and governance
Coordinate product, AI engineering, data science, architecture, operations and external partners across pilots and scaled deployments. Manage dependencies, quality and executive steering; oversee vendor and platform decisions and supervise an Agentic AI Delivery Principal.
Make launches stick
Lead launch planning, training, communications and hypercare. Measure adoption, gather feedback and maintain a post-launch improvement backlog across business units.

Likely challenges to think through

Choosing what to scale
Preparation hypothesis, not a stated employer problem: attractive use cases may have weak data access or limited portability. Prepare a prioritization approach that compares expected value with system constraints, delivery effort, quality and reuse before committing to a roadmap.
Balancing autonomy and control
Preparation hypothesis: more agent autonomy may improve speed but increase the consequences of incorrect actions or data exposure. Prepare to explain how you would choose human approval points, evaluate agent behavior and define escalation and release criteria with risk partners.
Coordinating the move beyond pilots
Preparation hypothesis: concurrent teams and external partners may face competing timelines and ownership boundaries. Prepare an example of resolving dependencies through clear decisions, acceptance criteria, delivery metrics and executive governance.
Separating adoption from impact
Preparation hypothesis: usage alone may not establish cost, revenue or customer-experience benefits. Prepare a measurement approach with a baseline, plausible attribution, adoption indicators and post-launch feedback that can guide investment decisions.

Practice your answers

Technical practice

How would you prioritize a multi-quarter portfolio of agentic AI use cases?

What to coverThe posting calls for a roadmap tied to business strategy and measurable outcomes, with trade-offs across value, quality, scalability, and portability. Suggested preparation: explain a prioritization approach that accounts for data and system constraints, delivery risk, and time to value.

How would you decide whether a user journey warrants an AI agent?

What to coverThe role includes diagnosing pain points, user journeys, and data and system constraints before framing a solution. Suggested preparation: describe how you would test the opportunity and choose between agent actions, human-in-the-loop steps, or a less autonomous approach.

When would you use RAG in an LLM-powered copilot or agent, and how would you evaluate it?

What to coverThe posting names RAG and experience evaluating production copilots or agents. Suggested preparation: discuss source quality, retrieval relevance, answer accuracy, and the trade-off between adding context and introducing unreliable material.

How would you design and test an agent that calls tools through MCP or APIs?

What to coverThe role requires understanding tool or function calling, agent actions, guardrails, and human-in-the-loop workflows. Suggested preparation: address permitted actions, failure handling, escalation, and when a person should approve an action before execution.

How would you evaluate an agent before release and diagnose a decline in quality afterward?

What to coverThe posting emphasizes evaluation, testing, release readiness, prompt and configuration management, and post-launch iteration. Suggested preparation: distinguish task-quality measures from business outcomes, then explain how you would investigate changes in prompts, configuration, data, or tool behavior.

What production controls would you put around an agent handling confidential data?

What to coverThe role partners with a Responsible AI leader on data handling, model and vendor risk, explainability, resilience, content filtering, and escalation. Suggested preparation: explain how you would define controls and review failures without assuming that any one guardrail eliminates risk.

How would you build a value case and measure whether an agent delivered its promised benefits?

What to coverThe posting specifies cost, revenue, customer experience, baselines, attribution, adoption, and continuous improvement. Suggested preparation: describe how you would separate usage from realized benefit and account for other changes that might affect the measured outcome.

How would you move concurrent agent initiatives from pilots to scaled deployment?

What to coverThe role coordinates cross-functional squads and partners while managing scope, dependencies, risks, delivery quality, and Agile or hybrid governance. Suggested preparation: explain the decision points, metrics, backlog and acceptance-criteria work, and evidence you would bring to executive steering.

How would you plan a launch and determine what to improve after go-live?

What to coverThe posting assigns launch planning, training, communications, hypercare, adoption measurement, and feedback loops to this role. Suggested preparation: discuss readiness signals, how you would triage early issues, and how feedback would shape a post-launch backlog.

How would you evaluate build, buy, or partner options for an agent platform?

What to coverThe role oversees vendor and platform decisions, contract scope, performance, and delivery outcomes. Suggested preparation: compare capability, integration constraints, portability, model and vendor risk, cost, and accountability for ongoing performance.

Behavioural practice

Tell me about a time you secured an executive decision on a complex technology investment.

What to coverThe posting calls for executive alignment, funding, and concise C-suite materials. Suggested preparation: use a real situation, your actions in presenting value and trade-offs, and the decision and measurable result.

Describe a time you aligned teams you did not directly manage across competing delivery priorities.

What to coverThe role requires influence without authority across product, engineering, data, operations, and other stakeholders. Suggested preparation: give the situation, how you resolved dependencies or disagreements, and the resulting delivery outcome.

Tell me about a time you escalated a quality, security, or compliance concern before launch.

What to coverThe posting emphasizes production guardrails, confidential data, risk management, and release readiness. Suggested preparation: explain the situation, your escalation and decision-making actions, and what changed as a result.

Describe a time you coached someone while remaining accountable for a demanding program.

What to coverThis role supervises an Agentic AI Delivery Principal and calls for mentoring cross-functional team members. Suggested preparation: provide a real situation, the coaching or delegation you undertook, and the effect on the person’s work and program delivery.

Tell me about a launch where adoption lagged expectations.

What to coverThe posting makes stakeholder readiness, training, hypercare, measurable adoption, and post-launch improvement part of the role. Suggested preparation: describe the situation, how you used feedback and evidence to adjust the rollout, and the observed result.

Sharpen your knowledge

Topics to prepare

Agent architecture and control
The posting explicitly calls for understanding RAG, MCP/API tool or function calling, autonomous agents, human-in-the-loop workflows, and prompt/configuration management. Revision exercise: compare how each would fit a hypothetical service journey, explaining data and system constraints and where human approval belongs.
Production agent evaluation
The role covers evaluation, deployment, iteration, and quality and accuracy metrics. Revision exercise: explain how you would evaluate an agent before launch and detect degraded results afterward, including when to stop or escalate its actions.
Responsible AI and data protection
The posting specifies confidential-data handling, model and vendor risk, explainability, resilience, content filtering, and escalation paths. Revision exercise: map these risks to controls for a hypothetical agent that can retrieve information and take actions.
Portfolio governance and delivery trade-offs
The leader owns a multi-quarter portfolio and uses Agile or hybrid delivery while managing scope, dependencies, risk, investment, and delivery metrics. Revision exercise: reason through a scenario in which two pilots compete for a shared engineering team and identify what an executive steering forum must decide.
Benefits attribution and adoption
The posting requires baselines, attribution, benefits measurement, adoption, and continuous improvement across customer experience, productivity, and operations. Revision exercise: distinguish deployment from realized benefit and explain how you would test whether an observed improvement came from an agent.
Operating-model and vendor decisions
The role advises executives on operating-model changes and oversees build/buy/partner decisions, contract scope, and partner performance. Revision exercise: compare those options for a hypothetical use case, identifying the decision rights and delivery risks each creates.

Skills to revise

Build a prioritized roadmap
The posting assigns ownership of an enterprise, multi-quarter use-case roadmap. Practice suggestion: rank three hypothetical agent opportunities against business value, scalability, quality, portability, and delivery constraints; show the trade-offs behind your sequencing.
Draft a defensible value case
The role builds business cases covering cost, revenue, and customer experience and tracks realized benefits. Practice suggestion: create a one-page case with assumptions, a pre-launch baseline, an attribution approach, adoption measures, and a point at which funding should be reconsidered.
Shape a production-ready backlog
The posting expects direct work with product, engineering, and data teams on backlog shaping, acceptance criteria, testing, and release readiness. Practice suggestion: write a few agent-use-case backlog items with observable acceptance criteria, evaluation cases, guardrails, and a release decision.
Run an executive decision forum
The role leads steering committees and produces concise C-suite materials across concurrent workstreams. Practice suggestion: prepare a one-page status brief showing outcomes, dependencies, risks, options, and the specific decision requested; rehearse a short explanation without technical jargon.
Plan launch and post-launch improvement
The posting names training, communications, hypercare, feedback loops, adoption, and a post-launch backlog. Practice suggestion: draft a launch checklist with stakeholder readiness, escalation ownership, adoption measures, feedback intake, and criteria for prioritizing improvements.
Structure a partner decision
The leader drives vendor and platform decisions and manages contract scope and performance. Practice suggestion: create a build/buy/partner comparison for a hypothetical agent deployment, including delivery dependencies, data-handling risk, performance measures, and unresolved questions for partners.

Your preparation checklist

Map the stated requirements to evidence
Prepare a one-page evidence map for the degree and experience requirements, including production AI/ML, generative AI, and agentic AI delivery. Use only examples and dates you can substantiate.
Build a production-agent walkthrough
If you have a relevant example, prepare a concise account of discovery, evaluation, deployment, and post-launch iteration. Show your decisions, cross-functional contributions, and measured results without disclosing confidential data.
Draft a multi-quarter portfolio exercise
Sketch how you would discover and prioritize several agent use cases using user journeys, data and system constraints, business value, scalability, quality, and portability. Label the use cases as hypothetical, not Ascensus plans.
Practice an agent design review
Draw a hypothetical solution showing where RAG, MCP or API tool calls, prompt configuration, and human approval might fit. Add evaluation criteria, acceptance tests, guardrails, and a rationale for the chosen level of autonomy.
Prepare an executive value case
Create a one-page example with cost, revenue or customer-experience assumptions, a baseline, an attribution approach, and adoption and quality measures. Show what evidence would justify funding or a change in direction.
Outline delivery and risk governance
Prepare a sample steering update covering workstreams, dependencies, decisions, metrics, and escalation. Include how you would partner with the Responsible AI leader on confidential data, model and vendor risk, explainability, and resilience, and how you would coach the Delivery Principal.
Plan launch through improvement
Draft a brief release-readiness and adoption plan covering testing, training, communications, hypercare, feedback, and a post-launch backlog; specify how you would measure uptake and benefits.
Write questions for the employer
Ask how portfolio priorities and funding decisions are made, what authority the role has over platform and vendor choices, how the Responsible AI partnership works, and which outcomes matter most after launch.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in United States. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…