Job Description: AI Program Manager

Account: Mashreq Bank

Location: India (Remote)

Experience: 10 15 Years

Employment Type: Full-time

1. Role Overview

The AI Program Manager is responsible for leading the end-to-end delivery of enterprise AI initiatives across the Mashreq Bank account. This role ensures the successful execution and adoption of Predictive AI, Generative AI, Agentic AI, and Intelligent Automation programs.

While the AI Architect designs the solutions, the AI Program Manager orchestrates their realization. You will bridge the gap between business stakeholders, AI Architecture, Data, Security, Platform Engineering, Governance, Risk, and Compliance teams. Your primary goal is to manage timelines, budgets, technical dependencies, and risks, ensuring that scalable, secure, and responsible AI solutions are delivered on time and aligned with Mashreq's strategic business outcomes.

2. Role Snapshot

Profile Area

Description

Role Title

AI Program Manager

Department

AI Delivery / AI Center of Excellence (CoE)

Primary Purpose

Drive the execution, governance, and delivery of enterprise AI programs (Predictive, GenAI, Agentic AI) while ensuring alignment with architectural standards and banking regulations.

Primary Stakeholders

Business Leaders, AI Architects, Data Engineers, Security, Platform Engineering, Risk, Compliance, Legal, and Delivery Squads.

3. Key Responsibilities 3.1 Program Delivery & Execution

- Lead the end-to-end lifecycle management of complex AI programs spanning predictive, generative, agentic, document intelligence, and automation domains.
- Develop and maintain comprehensive program plans, tracking milestones, dependencies, resource allocation, and budgets.
- Drive Agile/Scrum delivery methodologies across multiple cross-functional engineering and data science pods.
- Manage technical and operational dependencies across data pipelines, model training, application integration, and infrastructure layers.

3.2 Stakeholder Engagement & Alignment
- Act as the primary point of contact for Mashreq Bank business leaders, translating business priorities into actionable delivery roadmaps.




- Partner closely with AI Architects to ensure delivery squads are strictly adhering to defined enterprise AI reference architectures and design patterns (e.g., RAG, multi-agent systems).
- Facilitate steering committee meetings, providing transparent reporting on program health, ROI, and technical milestones to executive sponsors.

3.3 Risk, Governance, and Compliance Management
- Drive the operationalization of Responsible AI frameworks alongside Risk, Compliance, Legal, and Security teams.
- Ensure delivery timelines account for crucial architecture governance checkpoints, security reviews, and compliance sign-offs.
- Proactively identify and mitigate program risks, particularly those related to content safety, PII detection, AI hallucinations, and enterprise data protection.
- Track and manage technical debt and architecture decision records (ADRs) in collaboration with the architecture team.

3.4 Technology & Platform Readiness
- Coordinate the deployment of AI solutions across hybrid environments (Azure cloud and on-premises data centers).
- Manage the rollout schedules for foundational capabilities, including model serving infrastructure, MLOps/LLMOps/Agent Ops pipelines, and vector databases.
- Ensure smooth handover of AI solutions from build/delivery phases to IT operations and support teams, establishing robust SLAs and observability metrics.

4. Skills and Experience 4.1 Experience & Technical Fluency
- Total Experience: 10 15 years in IT project/program management, with at least 3 5 years specifically managing AI, Machine Learning, Data, or Cloud transformation programs.
- AI Domain Knowledge: Strong conceptual understanding of GenAI, Predictive AI, Agentic AI, RAG (Retrieval-Augmented Generation), and foundation models to effectively manage technical squads.
- Cloud & Infrastructure: Familiarity with cloud platforms (specifically Azure AI/Data services)



and hybrid deployment constraints (data residency, latency, secure interconnects).
- Lifecycle Management: Solid understanding of MLOps, LLMOps, and software development lifecycles (SDLC) for model deployment, continuous evaluation, and prompt engineering.
- Banking/Regulated Environments: Proven experience delivering technology programs in highly regulated sectors (Banking/Financial Services), with a deep understanding of data privacy, security-by-design, and compliance requirements.

4.2 Competency Matrix

Competency Area

Expected Capability

Program Management

Agile, SAFe, Scrum, capacity planning, budget management, roadmap development, and dependency mapping.

AI & Data Literacy

Foundational understanding of ML lifecycles, GenAI/Agentic AI concepts, RAG, semantic search, and MLOps/LLMOps.

Risk & Governance

Managing delivery within strict regulatory guardrails, tracking Responsible AI controls, privacy, and compliance gates.

Stakeholder Leadership

Executive reporting, cross-squad coordination, conflict resolution, vendor management, and steering committee facilitation.

Strategic Execution

Translating architectural blueprints and business goals into structured, phased delivery plans with measurable KPIs.

5. Qualifications

- Education: Bachelor's degree in Business, Computer Science, Information Technology, or a related field. Master's degree or MBA is a plus.
- Certifications:
- Active PMP, PgMP, PRINCE2, or Agile/Protected certifications are highly preferred.
- Certifications in Cloud Practitioner (Azure) or AI/ML Foundations are a strong advantage.

6. Success Profile
- Delivery-Obsessed: Consistently drives complex, multi-layered AI programs to completion on time and within budget.
- Translational Leader: Seamlessly bridges the gap between highly technical AI Architects/Engineers and business-focused banking stakeholders.
- Risk-Aware: Deeply respects the compliance, security, and governance requirements inherent to a major financial institution like Mashreq Bank.
- Adaptable: Thrives in navigating the fast-evolving landscape of Generative and Agentic AI, pivoting program strategies as underlying technologies mature.

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