New opportunity

Data/AI Director

MUFG Global Service Private Limited · Bengaluru, India

About this role

This director-level role at MUFG Global Service Private Limited leads an approximately 100-person global AI and Data organisation. The director will build production-grade AI and Data products that deliver measurable business value, while remaining a hands-on technical leader rather than serving only as a programme sponsor.

Responsibilities

Own AI and Data products from business problem definition and rapid prototyping through architecture, engineering, deployment, adoption and production operations. Guide model and platform selection, review designs and code, and help teams resolve retrieval, orchestration, data-quality, performance and production issues.
Set engineering direction for Generative AI solutions using approaches such as RAG, knowledge and context graphs, agentic systems, tool use and Model Context Protocol. Establish evaluation of output quality, grounding, retrieval, reliability, latency, cost, security, explainability and business impact.
Lead enterprise Data and AI platform architecture, including ETL and ELT, warehouses, lakes and lakehouses, data marts, virtualisation, metadata, lineage, batch and real-time processing, APIs and reusable data services. Support governed access to structured and unstructured information.
Hire, mentor and develop engineers and technical leaders; establish standards for design, code, testing, documentation, security and production readiness. Manage concurrent global initiatives, align senior stakeholders, promote reusable capabilities and track adoption and business outcomes.
Embed Responsible AI and bank-grade controls, including privacy, traceability, human oversight, access control, model and prompt evaluation, monitoring, fallback processes and third-party risk management.

Required qualifications

More than 15 years of relevant AI/ML, Generative AI, data engineering, software engineering, data-platform or advanced-analytics experience, including at least five years leading significant engineering teams, products or complex global initiatives. Candidates must have personally designed, built and shipped production-grade AI or Generative AI solutions within the last two to three years. Recent hands-on depth in several of RAG, graphs, agentic orchestration, tool-use frameworks, MCP, enterprise retrieval, LLM evaluation or secure enterprise integration is required, alongside technical decision-making, talent development, stakeholder management and risk-aware delivery.

Preferred qualifications

Leadership of a substantial combined AI and Data organisation or global centre of excellence; deep enterprise data-platform experience; delivery in financial services or another regulated industry; reusable AI platforms and governance; and global transformation experience. A master’s degree or higher in a related technical discipline is preferred; a PhD is advantageous but not essential for candidates with exceptional practical engineering achievements.

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

Skills for this role

Generative AIMachine LearningData EngineeringAI Product DevelopmentRetrieval-Augmented GenerationKnowledge GraphsContext GraphsAI AgentsMulti-Agent SystemsAI OrchestrationTool-Use FrameworksModel Context ProtocolEnterprise SearchLLM EvaluationModel ObservabilityAI GuardrailsHuman-in-the-Loop WorkflowsSolution ArchitectureCode ReviewData ArchitectureETLELTData WarehousingData LakesLakehouse ArchitectureData MartsData VirtualisationMetadata ManagementData LineageReal-Time Data Processing

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

Lead AI engineering from the front
The posting requires recent personal involvement in shipping production AI or Generative AI and expects the director to review designs and code, make architecture decisions, and help resolve production issues. For preparation, be ready to explain specific technical decisions and your direct contribution, rather than only programme oversight.
Own products through adoption and measurable value
The role owns AI and Data products from business problem definition and prototyping through deployment, adoption, operations, and value realisation. Preparation should connect product choices and evaluation measures—such as grounding, reliability, latency, cost, and business impact—to decisions made after launch.
Build governed, reusable Data and AI platforms
The director sets architecture direction across data integration, warehouses, lakes or lakehouses, metadata, lineage, APIs, and access to structured and unstructured information. For preparation, focus on how architecture choices support both analytics and AI workloads without unnecessary complexity.
Lead a global engineering organisation
The posting assigns leadership of approximately 100 AI and Data professionals, concurrent global initiatives, senior-stakeholder alignment, hiring, and development of technical leaders and specialists. Preparation should address how priorities, technical standards, and accountability can remain clear at that scale.
Embed Responsible AI and bank-grade controls
The role calls for privacy, access control, traceability, human oversight, evaluation, monitoring, fallback processes, and third-party risk management throughout delivery. Preparation should show how controls are designed into a product and its operations, not added only at release.

Likely challenges to think through

Experiment quickly without weakening controls
Likely challenge, not a confirmed employer problem: rapid prototyping can conflict with the security, auditability, and operational resilience expected in a financial institution. Prepare a decision approach that distinguishes low-risk experiments from production use and sets evaluation, oversight, and release criteria for each.
Invest in shared platforms while delivering near-term use cases
Likely challenge, not a confirmed employer problem: reusable enterprise capabilities take investment, while business teams need timely outcomes. Prepare to explain when to build a common service, when a narrower solution is sufficient, and how value, reuse potential, risk, and capacity inform that choice.
Stay technically credible while leading at scale
Likely challenge, not a confirmed employer problem: direct technical involvement competes with responsibility for an approximately 100-person organisation and multiple global initiatives. Prepare to describe where a director should personally review or troubleshoot, what to delegate to technical leaders, and how to spot issues without becoming a delivery bottleneck.

Practice your answers

Technical practice

How would you take an AI use case from business problem definition to a production product?

What to coverSuggested answer focus: Define the business outcome and adoption measure before prototyping, then explain architecture, engineering, deployment and operational ownership. Discuss when to stop an experiment and what evidence would justify scaling it.

How would you design and troubleshoot retrieval-augmented generation over governed enterprise information?

What to coverSuggested answer focus: Explain how you would handle access-controlled sources, retrieval relevance, grounding and source traceability. Distinguish retrieval failures from model-behaviour failures and describe how you would test each.

When would a knowledge or context graph improve an AI solution?

What to coverSuggested answer focus: Compare graph-based context with simpler retrieval for relationships that matter to the use case. Address data quality, maintenance and whether the added complexity produces measurable improvement.

How would you decide whether an AI workflow needs agents, tool use or MCP-style integration?

What to coverSuggested answer focus: Explain what warrants orchestration rather than a simpler workflow, and how you would constrain tool access and actions. Cover reliability, traceability, human oversight and the operational cost of additional components.

How would you evaluate a Generative AI product before and after release?

What to coverSuggested answer focus: Describe tests for output quality, grounding, retrieval effectiveness, security and explainability alongside latency, cost and reliability. Explain how monitoring and business-impact measures would inform changes in production.

How would you architect a data platform that serves both analytics and AI workloads?

What to coverSuggested answer focus: Compare appropriate uses of ETL and ELT, warehouses, lakes or lakehouses, batch and real-time processing, and reusable APIs or data services. Explain how governed access to structured and unstructured information affects the design.

How would you make enterprise data trustworthy and governable across products?

What to coverSuggested answer focus: Cover data definitions, ownership, quality checks, metadata, lineage, retention and access control. Explain how teams would detect and resolve quality issues without creating a separate, inconsistent governance approach for each use case.

What controls would you build into a bank-grade AI product?

What to coverSuggested answer focus: Address privacy, security, auditability, content safety, model and prompt evaluation, human decision points and third-party risk. Explain how monitoring, escalation and fallback would work when the system behaves unexpectedly.

A deployed AI product has declining answer quality and rising latency. How would you diagnose it?

What to coverSuggested answer focus: Separate possible changes in source data, retrieval, orchestration, model behaviour and system performance using production evidence. Explain how you would contain user impact, test a correction and verify recovery.

What would you require before promoting a successful AI prototype to a reusable production capability?

What to coverSuggested answer focus: Discuss architecture and code reviews, testing, documentation, security, resilience and clear operational ownership. Weigh reuse and maintainability against the risk of building an unnecessarily complex platform before demand is established.

Behavioural practice

Tell me about a time you personally helped resolve a difficult AI or data engineering decision.

What to coverSuggested example structure: Set out the situation and your direct technical actions, such as reviewing a design or diagnosing a production issue, rather than describing sponsorship alone. Give the result and what evidence showed the decision worked.

Tell me about a time you developed senior technical talent or raised an engineering team's standards.

What to coverSuggested example structure: Describe the team's situation, your actions in hiring, mentoring or establishing review practices, and the result. Include how you assessed progress for specialists or technical leaders, not only managers.

Tell me about a time global stakeholders disagreed on an AI or data direction.

What to coverSuggested example structure: Explain the competing priorities, how you communicated technical options and risks in business terms, and what you did to reach a decision. State the result, including any unresolved trade-off.

Tell me about a time you reprioritised several concurrent technical initiatives.

What to coverSuggested example structure: Describe the capacity or dependency constraints, your actions to compare business value, feasibility, risk and reuse, and the resulting delivery or adoption outcomes. Make your own decision-making and accountability clear.

Tell me about an experiment you stopped or changed while preserving momentum and appropriate controls.

What to coverSuggested example structure: Explain the initial hypothesis and success or stop criteria, what the evidence showed, and the action you took with the team and stakeholders. Describe the result for business value, learning or risk, without presenting experimentation itself as the outcome.

Sharpen your knowledge

Topics to prepare

Production AI product lifecycle
Posting: The director owns products from business problem definition through deployment, adoption and operations, and must have personally shipped a production AI or Generative AI solution recently. Revision exercise: map those stages for a real project you can discuss, separating your direct technical decisions from team contributions.
Enterprise retrieval and RAG trade-offs
Posting: Recent hands-on depth in several areas, including RAG and enterprise retrieval, is required. Revision exercise: compare two retrieval designs for governed enterprise information and explain when grounding, access control or retrieval quality would change your choice.
Graphs, agents and integration boundaries
Posting: Knowledge or context graphs, agentic orchestration, tool use and MCP or similar standards are named areas of depth. Revision exercise: decide which of these patterns a proposed use case actually needs, and explain the added complexity and security boundaries.
Generative AI evaluation and operations
Posting: Evaluation must cover output quality, grounding, retrieval, reliability, latency, cost, security, explainability and business impact. Revision exercise: define what evidence would justify moving one prototype into production and what failures would stop it.
Enterprise data-platform architecture
Posting: The role leads platforms spanning ETL and ELT, warehouses, lakes or lakehouses, metadata, lineage, batch and real-time processing, and governed AI access. Revision exercise: compare architecture options for serving both analytics and unstructured-information retrieval, including reuse and operational trade-offs.
Bank-grade governance and global leadership
Posting: The director leads an approximately 100-person global AI and Data organisation while embedding Responsible AI, risk controls, talent development and measurable outcomes. Revision exercise: rank three competing initiatives by value, risk, reuse and capacity, then explain the controls and leadership decisions behind the ranking.

Skills to revise

Whiteboard an end-to-end AI solution
Posting: The director must be able to design solutions and defend architecture, model and platform choices. Practice drawing a use case from data access and prototyping through deployment, adoption and production support; state your trade-offs and measurable outcome.
Diagnose a production failure
Posting: Hands-on troubleshooting of retrieval, orchestration, data quality, performance and model behaviour is expected. Practice tracing a hypothetical poor answer to its likely cause, identifying checks, a safe fallback and a remediation plan.
Conduct a technical review
Posting: The role participates in architecture, code and pull-request reviews and sets engineering standards. Practice reviewing a sample design or code change for maintainability, testing, security, traceability and production readiness; write specific review comments.
Build an evaluation and control plan
Posting: Model and prompt evaluation, observability, guardrails, human oversight and escalation are explicit responsibilities. Practice drafting test cases, monitoring measures, access checks and human decision points for a hypothetical enterprise AI workflow.
Communicate portfolio decisions
Posting: The director manages concurrent global initiatives, develops technical leaders and explains trade-offs to senior stakeholders. Practice a brief decision briefing that prioritises initiatives, names accountable owners, addresses talent capacity and reports adoption and business-value measures.

Your preparation checklist

Map evidence to the stated requirements
The posting requires more than 15 years of relevant experience, at least five years of leadership, and recent hands-on production AI delivery. Prepare a dated evidence sheet with specific roles, contributions and outcomes; leave unsupported claims out.
Build a recent production AI walkthrough
The role calls for personally shipping production-grade AI or Generative AI within the last two to three years. Prepare one project account covering the business problem, your direct design and engineering decisions, deployment, adoption, operations and measured value.
Rehearse an end-to-end AI design
The posting expects credible whiteboarding and technical troubleshooting. Practice a hypothetical enterprise AI design showing when to use RAG, graphs or tool-based orchestration, how to secure integrations, and how you would diagnose retrieval or model-behaviour failures.
Prepare an evaluation and production-readiness plan
For that hypothetical design, draft a short plan for testing grounding, retrieval effectiveness, output quality, reliability, latency and cost. Include monitoring, ownership and criteria for moving beyond a prototype; these are preparation choices, not a stated interview exercise.
Sketch a governed data-platform architecture
The role covers both traditional analytics and AI workloads. Prepare a diagram connecting relevant ETL or ELT, warehouse or lakehouse, batch or real-time processing, metadata, lineage and reusable services, with governed access to structured and unstructured data.
Show how controls shape delivery
The posting calls for bank-grade Responsible AI and data governance. Prepare a control map for a hypothetical use case covering privacy, access, auditability, human oversight, prompt and model evaluation, monitoring, fallback and third-party risk.
Select leadership and prioritisation examples
Prepare concise examples, where available, of hiring and developing senior technical talent, setting engineering standards, and prioritising concurrent global initiatives. State the trade-offs, stakeholder decisions, adoption measures and business outcomes in each.
Write questions for the employer
Ask which AI and Data outcomes matter most, how the approximately 100-person organisation is structured, where shared platforms need improvement, and how global stakeholders balance rapid experimentation with risk and production ownership.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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