New opportunity

Technical Project Manager/ Sr. Associate Director, Delivery Management

HSBC · Pune, India

About this role

Role Purpose: HSBC is seeking an experienced professional to join as a Sr. Associate Director, Delivery Management to oversee the end-to-end delivery of AI initiatives. The role involves managing programme execution across internal tooling, reusable platforms, and customer-facing AI solutions. Responsibilities: Oversee AI initiatives from discovery through implementation, governance, and launch. Manage delivery control, including RAID, dependency resolution, and status reporting. Shape AI use cases by defining objectives, success criteria, and production roadmaps. Coordinate across engineering, architecture, data, cyber, risk, and compliance teams. Translate complex AI concepts like Agentic AI, RAG, and LLMs into business language. Monitor operational performance and capture lessons learned. Requirements: Proven experience in delivering AI, data, or technology use cases with measurable outcomes. Strong background in turning strategic priorities into structured delivery plans. Established capability in coordinating across cross-functional teams including technology, architecture, and risk. Solid track record of RAID management and delivery governance. Working knowledge of modern AI concepts including LLMs, RAG, and agentic patterns. Experience operating in governance and control environments covering cyber, model risk, and responsible AI. Excellent communication skills for senior stakeholder engagement.

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Skills for this role

Delivery ManagementAI InitiativesRAID ManagementStakeholder ManagementAgentic AIRAGLLMsGovernanceProject ExecutionRisk ManagementCompliance

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

End-to-End AI Initiative Delivery
The role requires managing AI projects from discovery through production launch. You must demonstrate the ability to translate strategic business objectives into structured, actionable delivery plans while ensuring measurable outcomes and production readiness.
Cross-Functional Governance and Stakeholder Alignment
You must coordinate across diverse teams including engineering, architecture, cyber, risk, and compliance. A core priority is navigating complex internal governance and control environments to ensure AI solutions meet enterprise standards for responsible AI and model risk.
Technical Translation and Communication
You are expected to bridge the gap between technical AI concepts—such as Agentic AI, RAG, and LLMs—and business stakeholders. This involves facilitating decision-making by presenting complex technical risks and progress in clear, business-oriented language.
Rigorous Delivery Control and RAID Management
Maintaining visibility and control is essential. You must proactively manage Risks, Assumptions, Issues, and Dependencies (RAID) and provide transparent status reporting to senior leadership to unblock issues at pace.

Likely challenges to think through

Balancing Innovation with Strict Banking Governance
HSBC operates in a highly regulated environment. The challenge is to accelerate the delivery of modern AI solutions without compromising on cyber, model risk, or compliance requirements. Prepare by highlighting your experience in embedding control frameworks into agile delivery cycles.
Managing Ambiguity in Emerging AI Technologies
AI initiatives, particularly those involving Agentic AI and RAG, often face technical uncertainty. You will need to define success criteria for projects where requirements may evolve. Prepare by discussing how you establish reusable delivery patterns to reduce lead times while maintaining quality.
Coordinating Diverse Stakeholder Interests
Aligning technical teams with risk and compliance functions can lead to friction. You must demonstrate the ability to facilitate consensus across disparate groups. Prepare by sharing examples of how you have successfully navigated conflicting priorities in a large-scale enterprise setting.

Practice your answers

Technical practice

Managing AI Delivery Lifecycle

What to coverDescribe your approach to overseeing AI initiatives from discovery to production. Focus on how you balance the need for rapid experimentation with the rigorous governance and compliance requirements typical of a large financial institution.

Translating AI Concepts for Stakeholders

What to coverExplain how you communicate complex technical concepts like Agentic AI, RAG, or LLM limitations to non-technical business leaders. Provide examples of how you align these technical capabilities with specific business objectives and success criteria.

RAID Management in AI Projects

What to coverDiscuss your methodology for managing Risks, Assumptions, Issues, and Dependencies (RAID) in high-uncertainty AI projects. Emphasize how you identify and escalate blockers early to maintain project momentum.

Navigating Governance and Model Risk

What to coverDetail your experience working within strict regulatory and control environments. Explain how you coordinate across cyber, architecture, and risk teams to ensure AI solutions meet enterprise standards for responsible AI.

Defining AI Success Metrics

What to coverHow do you define and measure success for AI initiatives beyond simple technical performance? Suggestion: Focus on how you track business value, operational performance, and user adoption post-implementation.

Scaling Reusable AI Platforms

What to coverDescribe your experience in building or managing reusable AI platforms or internal tooling. Explain how you balance the need for standardized, scalable infrastructure with the specific requirements of individual business use cases.

Dependency Management in Cross-Functional Teams

What to coverHow do you coordinate delivery across diverse teams like engineering, data science, and compliance? Explain your strategy for aligning these groups to a unified roadmap while managing conflicting priorities.

Monitoring and Continuous Improvement

What to coverOnce an AI solution is in production, how do you monitor its performance and capture lessons learned? Explain how you use this feedback loop to drive iterative improvements and reduce future lead times.

Behavioural practice

Handling Conflicting Stakeholder Priorities

What to coverDescribe a situation where you had to manage competing demands from technical teams and business stakeholders. Explain the actions you took to reach a consensus and the final result for the project.

Overcoming Significant Delivery Roadblocks

What to coverProvide an example of a time when a critical dependency or risk threatened an AI project's timeline. Detail the steps you took to unblock the situation and how you communicated the impact to senior leadership.

Influencing Without Direct Authority

What to coverShare an experience where you needed to influence cross-functional teams to adopt a new governance process or delivery standard. Describe your approach to gaining buy-in and the measurable outcome of your efforts.

Adapting to Changing Strategic Priorities

What to coverTell us about a time when the strategic direction of a project shifted mid-delivery. Explain how you restructured your delivery plan and kept the team aligned during the transition.

Sharpen your knowledge

Topics to prepare

AI Governance and Risk Frameworks
The role requires navigating complex control environments including model risk, cyber security, and responsible AI. Exercise: Review standard AI risk management frameworks (e.g., NIST AI RMF) and map them to the specific requirements of a highly regulated financial institution to understand how to balance innovation with compliance.
AI Delivery Lifecycle Management
The position involves overseeing AI initiatives from discovery to production. Exercise: Create a hypothetical project roadmap for an LLM-based internal tool, identifying key milestones, necessary cross-functional dependencies (e.g., data privacy, architecture), and potential failure points at each stage.

Skills to revise

RAID Management for AI Projects
The role demands a solid track record of managing Risks, Assumptions, Issues, and Dependencies. Exercise: Develop a sample RAID log for a RAG-based project, specifically focusing on technical dependencies like data quality and model latency, and draft an escalation plan for a critical blocker.
Translating AI Concepts for Stakeholders
You must explain Agentic AI, RAG, and LLMs to non-technical business leaders. Exercise: Draft a one-page executive summary explaining the business value and operational risks of an Agentic AI workflow, avoiding overly technical jargon while focusing on measurable outcomes and production readiness.
Cross-Functional Coordination
The role requires aligning engineering, architecture, risk, and compliance teams. Exercise: Simulate a stakeholder meeting agenda where you must resolve a conflict between a rapid deployment timeline and a strict model risk compliance requirement, focusing on evidence-based decision facilitation.

Your preparation checklist

Prepare an AI project delivery case study
Select a past AI or data project you led. Prepare to explain the end-to-end lifecycle, specifically how you managed RAID (Risks, Assumptions, Issues, Dependencies), resolved cross-functional blockers, and ensured the final output met business objectives.
Develop a framework for translating AI concepts
Practice explaining technical concepts like RAG (Retrieval-Augmented Generation), Agentic AI, and LLM evaluation metrics to non-technical stakeholders. Prepare a concrete example of how you simplified a complex technical hurdle to secure executive buy-in or budget approval.
Map your experience to governance and control environments
Review your history of working within highly regulated sectors. Prepare to discuss how you navigated model risk, cyber security, and responsible AI compliance requirements, focusing on how you balanced delivery speed with strict enterprise governance standards.
Formulate questions on HSBC's AI delivery model
Prepare insightful questions for the interviewers regarding how HSBC currently manages the transition from AI experimentation to production. Ask about their specific approach to cross-functional coordination between engineering, risk, and compliance teams for AI initiatives.
Structure your delivery methodology
Be ready to describe your approach to creating reusable delivery patterns. Explain how you have previously reduced lead times in technology projects while maintaining high quality and meeting rigorous internal control requirements.

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