Description

Primary Purpose
Plan, coordinate, and deliver AI initiatives by bridging business, technology, data, governance, and delivery teams across the AI solution lifecycle. 
Primary Stakeholders Business teams, AI CoE, Technology, Data, Architecture, Security, Risk,
Compliance, Legal, Operations, and implementation partners.

3. Key Responsibilities
3.1 AI Use Case Planning and Delivery Management
• Define project scope, objectives, milestones, timelines, dependencies, and delivery plans for AI initiatives.
• Translate business priorities into structured AI delivery plans with clear outcomes, success measures, and governance checkpoints.
• Manage end-to-end delivery across discovery, feasibility assessment, solution design, development, testing, deployment, and post-implementation review.
• Track project progress, risks, issues, decisions, dependencies, change requests, and escalations through structured RAID governance.
• Ensure delivery remains aligned to approved scope, budget, timelines, business value, and enterprise AI governance requirements.
3.2 Techno-Functional Solution Coordination
• Bridge business, product, data science, engineering, architecture, security, and governance teams to ensure shared understanding of requirements and solution direction.
• Facilitate requirement workshops, solution walkthroughs, design discussions, sprint ceremonies, and stakeholder reviews.

• Support translation of business requirements into user stories, functional specifications, acceptance criteria, test scenarios, and operational readiness needs.
• Maintain sufficient technical understanding of AI, data, integration, model lifecycle, cloud, and security concepts to challenge assumptions and coordinate delivery effectively.
3.3 Governance, Risk, and Compliance Coordination
• Coordinate AI governance reviews, risk assessments, security reviews, privacy assessments, model validation, and approval checkpoints.
• Ensure AI initiatives comply with enterprise standards, responsible AI principles, regulatory expectations, data protection requirements, and internal control frameworks.
• Track closure of governance actions, audit observations, policy exceptions, technical risks, and control gaps before production release.
• Maintain delivery documentation including project plans, RAID logs, decision records, governance packs, approval notes, status reports, and closure reports.
3.4 Responsible AI, Testing, and Readiness
• Coordinate responsible AI testing, model evaluation, guardrail validation, user acceptance testing, operational readiness, and production go-live preparation.
• Ensure project teams define measurable acceptance criteria covering accuracy, reliability, explainability, safety, fairness, privacy, resilience, monitoring, and human oversight.
• Support transition to operations by coordinating runbooks, support models, monitoring dashboards, incident processes, user training, and benefits tracking.
3.5 Partner, and Resource Management
• Coordinate internal teams, vendors, implementation partners, and platform providers to ensure timely delivery of agreed outcomes.
• Manage resource plans, delivery commitments, dependencies, statements of work, deliverables, acceptance criteria, and vendor performance reviews.
• Ensure third-party AI tools, platforms, and services follow enterprise onboarding, security, procurement, legal, and governance requirements.
3.6 Reporting, Communication, and Executive Updates
• Prepare concise project updates, steering committee packs, AI Council submissions, executive dashboards, and delivery health reports.
• Communicate risks, decisions, trade-offs, delivery constraints, and required management actions in a clear and business-friendly manner.
• Maintain stakeholder alignment through regular governance forums, delivery stand-ups, working groups, and escalation channels.
• Support decision-making by presenting delivery options, impacts, benefits, risks, and dependencies to senior stakeholders.
3.7 Change Adoption and Business Enablement
• Support business readiness, user engagement, training coordination, communications, adoption tracking, and operating model changes for AI solutions.
• Coordinate with business owners to confirm process impacts, user roles, controls, exception handling, and success measures.
• Ensure benefits realization is tracked through agreed KPIs, value measures, operational metrics, and post-go-live adoption reviews.
• Promote responsible and sustainable adoption of AI across business functions.
3.8 Continuous Improvement and Delivery Maturity
• Improve AI delivery playbooks, templates, governance workflows, reporting standards, and delivery assurance practices.
• Capture lessons learned from AI initiatives and convert them into reusable guidance for future projects.
• Track emerging delivery risks, AI project management practices, toolsets, automation opportunities, and governance improvements.
• Contribute to the maturity of enterprise AI delivery, portfolio management, and project assurance capabilities.
4. Skills and Experience
4.1 Technical Skills
• Strong understanding of AI delivery lifecycles across discovery, feasibility, design, build, testing, governance approval, deployment, monitoring, and benefits realization.
• Working knowledge of predictive AI, generative AI, agentic AI, RAG, data pipelines, model lifecycle, model evaluation, guardrails, LLMOps, MLOps, AgentOps, and AI monitoring concepts.
• Ability to translate business requirements into project scope, user stories, functional specifications, acceptance criteria, test plans, delivery milestones, and governance documentation.
• Strong project management capability across planning, scheduling, budgeting, dependency management, RAID management, reporting, change control, stakeholder management, and delivery assurance.
• Experience coordinating cross-functional teams including business, product, technology, architecture, data science, engineering, security, risk, compliance, legal, operations, and vendors. 
• Good understanding of responsible AI controls including fairness, explainability, privacy, security, human oversight, content safety, prompt injection mitigation, auditability, and traceability.
• Experience managing agile, hybrid, or waterfall delivery models and coordinating sprint planning, backlog grooming, release planning, testing cycles, and production readiness.
• Familiarity with cloud, data, integration, API, security, identity, DevOps, CI/CD, observability, and operational support concepts relevant to enterprise AI solutions.
• Ability to prepare executive-level status reports, steering committee materials, AI Council submissions, governance packs, project closure reports, and benefits realization updates.
• Strong communication skills with the ability to explain technical risks, business impacts,delivery options, and governance requirements to senior and non-technical stakeholders.
• Experience working with vendors and implementation partners, including SOW tracking, deliverable acceptance, dependency management, performance monitoring, and issue escalation.
• Good understanding of regulated banking environments, including control documentation, audit readiness, data protection, operational resilience, and technology risk management.

4.2 Competency Matrix
Competency Area Expected Capability
AI & Data Predictive AI, generative AI, agentic AI, RAG, model lifecycle, data pipelines, evaluation, guardrails, monitoring, AI testing, and AI delivery lifecycle practices.
Project Delivery Project planning, milestone tracking, RAID management, dependency management, change control, delivery assurance, governance reporting, release coordination, and closure management.
Governance & Risk AI governance coordination, responsible AI controls, privacy, security, compliance alignment, risk assessments, approval checkpoints, audit readiness, and control documentation.
Delivery Leadership Cross-functional coordination, stakeholder engagement, workshop facilitation, vendor management, executive reporting, escalation management, and adoption enablement.
Strategic Thinking Business-value orientation, delivery pragmatism, benefits realization, continuous improvement, AI portfolio maturity, risk-aware decision-making, and enterprise alignment.
5. Qualifications
Bachelor’s degree in Computer Science, Information Technology, Information Systems,
Engineering, Business Administration, Project Management, Data Science, or a related discipline.
Preferred qualifications include a master’s degree or equivalent practical experience, along with relevant certifications such as PMP, PRINCE2, Agile, Scrum, SAFe, AI/ML, cloud, data, technology risk, or governance certifications.
6. Experience
• 8–12+ years of overall experience in project management, technology delivery, business transformation, digital delivery, data, or AI-enabled change initiatives.
• 4+ years managing technology, data, analytics, automation, or AI-related projects in complex enterprise environments.
• Experience coordinating cross-functional teams across business, technology, data science, engineering, architecture, security, risk, compliance, and vendor organizations.
• Experience working within regulated, security-conscious, or governance-driven environments, preferably in banking or financial services.
 

Key Skills

Education

Any Graduate