BU EA QA

“Tell Me About Yourself”

First of all, thank you for your time and for this opportunity — it’s a real pleasure to speak with you today. I’m Anand Nerurkar, currently serving as VP of Technology, with over 21 years of experience spanning Enterprise Architecture, full-stack engineering, and large-scale transformation delivery. Over the past decade in the BFSI domain, I’ve led strategic modernization programs — including digital lending, mutual fund platform transformation, payment automation, and core banking modernization. These initiatives have delivered clear business impact — reducing loan turnaround time from one week to a single day, improving release frequency from eight weeks to two, and enabling real-time regulatory compliance — all while improving customer and partner experience. My approach combines strategic vision with hands-on architecture execution. I work closely with CXOs, business, and compliance heads to translate business drivers into clear architecture roadmaps and capability maps. From there, I define technology strategy, design reference architectures, and establish architecture governance to ensure every solution aligns with business goals, risk controls, and regulatory standards. In essence, I focus on bridging business strategy with technology execution, ensuring architecture delivers tangible and measurable value. On the technology side, I stay hands-on with enterprise architecture patterns, cloud-native platforms (Azure, AWS, GCP), and GenAI adoption — particularly around explainability, bias and fairness validation, and responsible AI guardrails for BFSI use cases such as KYC automation, policy summarization, and fraud detection. I also take pride in mentoring architecture and engineering teams, driving alignment, innovation, and execution excellence across the organization. I’m very excited about the opportunity to contribute to BFSI transformation journey, positioning technology as a strategic enabler and helping our clients achieve measurable business outcomes through architecture-led modernization.

What’s your approach to Enterprise Architecture?

Answer (structured – 4-step model):

My approach to Enterprise Architecture is both business-outcome-driven and governance-led.

Step 1 – Understand Business Drivers:I begin by engaging CXOs, BU heads, and compliance leaders to understand their strategic goals, challenges, and KPIs — whether it’s faster time-to-market, cost optimization, or regulatory adherence.

Step 2 – Translate to Capabilities & Architecture Roadmap:I map business objectives into capability models and architecture blueprints — defining what business, data, application, and technology capabilities are needed to support those outcomes.

Step 3 – Define Standards & Governance:I establish principles, reference architectures, and reusable patterns (e.g., microservices, API-first, event-driven) and set up an EA governance board to ensure compliance and consistency across programs.

Step 4 – Enable Execution & Value Realization:I partner with delivery and DevOps teams to ensure architecture is implementable, cost-effective, and measurable, tracking business KPIs such as release frequency, SLA adherence, cost-to-serve, and compliance score.

In short, EA is not just a design discipline — it’s a strategic enabler that connects business ambition with technology execution.

3️⃣ Can you walk me through one BFSI transformation you led end-to-end?

Example: Digital Lending Modernization

Certainly. One of the key programs I led was a Digital Lending Modernization initiative for a leading private bank.

Business Problem: Loan processing was manual and took up to a week, impacting customer experience and credit conversion.

My Role: As Enterprise Architect, I led end-to-end architecture definition and governance.

Approach:

1.Conducted workshops with business, operations, and risk teams to capture pain points and map the value stream. Defined a capability model covering origination, KYC, credit assessment, agreement, and disbursement. Designed a microservices-based architecture on Azure Cloud, integrating Spring Boot services, Kafka, and RPA for document processing. Implemented a GenAI-powered document summarization layer for KYC and policy extraction, ensuring bias, explainability, and hallucination validation were built in using Spring AI + Azure OpenAI guardrails. Set up architecture governance with sprint reviews, design authority, and cost optimization checklists.

Business Outcome: Reduced loan turnaround from 7 days to 1 day, Increased release frequency from 8 weeks to 2 weeks, Achieved 100% compliance with new regulatory mandates, Improved customer satisfaction scores by 30%. This transformation became a template for other loan products within the bank.

4️⃣ How do you ensure Responsible AI / GenAI adoption in BFSI?

Answer:

Responsible AI is critical in financial institutions due to governance, bias, and regulatory risks. My approach combines technical controls and governance processes: Prompt Validation Layer: Ensures inputs adhere to compliance and security guardrails (no PII or confidential data leakage). Bias & Fairness Evaluation: Each model output is tested against fairness metrics — avoiding gender, location, or product bias. Hallucination & Explainability Checks: Using Azure OpenAI + Spring AI, responses are validated via a RAG layer to verify factual accuracy before output. Audit & Feedback Loops: Every interaction is logged for traceability and used to continuously retrain models based on compliance feedback. Governance Board: Aligns AI outcomes with Responsible AI principles, regulatory norms, and internal risk frameworks. This ensures AI remains transparent, fair, and auditable, especially in compliance-heavy BFSI environments.

5️⃣ How do you align architecture with business KPIs?

Answer:

I always link architecture outcomes to measurable business KPIs. For example: Operational Efficiency: Reduce TAT, automate manual processes. Resilience & Compliance: Improve uptime, meet regulatory SLAs. Customer Experience: Faster digital onboarding, 24x7 availability. Cost Optimization: Move from CAPEX to OPEX via cloud-native adoption. I use Architecture Value Scorecards to track these KPIs across projects — ensuring EA is a business lever, not just a design function.

6️⃣ How do you manage multiple business units or stakeholders?

Answer:

I use a federated architecture engagement model. Define central architecture standards (principles, blueprints, security patterns). Enable BU-specific solution architects to adapt these for their unique needs. Run a monthly Architecture Review Board (ARB) to ensure alignment and cross-BU synergy. Track architecture maturity, reusability, and cost metrics across BUs. This model ensures both consistency and agility — every BU innovates independently, but under a common architectural vision.

7️⃣ What’s your leadership philosophy as an Enterprise Architect?

Answer:

My philosophy is to lead through influence, not authority. I believe Enterprise Architects are strategic enablers and coaches, not gatekeepers. I focus on: Empowering teams to make sound design decisions guided by architecture principles. Mentoring architects and tech leads, ensuring they understand business context, not just tech. Driving collaboration between business, IT, and operations — because architecture succeeds when all three align. My goal is always to connect technology strategy to business success, and help organizations move from vision to measurable impact.

8️⃣ What excites you about ABC opportunity?

Answer:

ABC is at a pivotal point — combining deep domain experience with next-gen digital and cloud transformation. What excites me most is the chance to work across multiple BFSI business units, drive enterprise modernization at scale, and help ABC clients adopt GenAI, Cloud, and API-driven architectures responsibly. I see this role as an opportunity to help shape the enterprise blueprint, build strong governance models, and create reusable patterns that can accelerate transformation across clients. I want to be part of journey where architecture becomes a true business differentiator.

✅ EA Governance Model Required to Enable AI/ML & GenAI at Scale

To successfully adopt AI/ML & GenAI across an enterprise — especially in BFSI where security, risk, compliance, and explainability matter — EA must establish a formal governance layer across people, process, and platforms.

Below is the 8-pillar Governance Framework you should describe.

✅ 1. Strategic Alignment & AI Value Governance

Ensure GenAI programs directly support business goals.

What EA sets up:

• AI Strategy & Roadmap

• AI Value Framework (business outcomes, KPIs, ROI)

• Use Case Prioritization Model (risk × value × readiness)

• Executive Steering Committee (CIO, CDO, CRO, Compliance, Business Heads)

Why:Prevents random experiments; ensures AI delivers measurable business impact.

✅ 2. Architecture Standards & Reference Blueprints

Define the architectural guardrails that all teams must follow.

Includes:

• GenAI Reference Architecture (RAG, vector DB, models, API gateway, monitoring)

• MLOps & LLMOps Reference Blueprint

• Patterns for Prompt Orchestration, Agent Frameworks, RAG pipelines

• Integration standards with Core Banking, Data Lake, APIs, Kafka, CRM

• Cloud-agnostic patterns for Azure OpenAI / AWS / GCP Vertex

Why:Ensures every AI solution is consistent, secure, scalable, and compliant.

✅ 3. Data Governance for AI (Mandatory in BFSI)

AI is only as good as the data behind it.

EA must define:

• Data quality, lineage, catalog, metadata governance

• PII masking, tokenization, anonymization

• Golden source identification

• Data retention & archival for AI models

• Data contracts for AI pipelines

Why:Ensures responsible use of sensitive banking data.

✅ 4. AI Model Governance (Responsible AI)

This is the biggest gap in most organizations.

EA Governance must set up:

✅ Model Risk Classification

(low, medium, high risk depending on business impact)

✅ Model Lifecycle Governance

• approval for training

• implementation

• performance monitoring

• periodic reviews

• explainability validation

✅ Guardrail Policies

• Hallucination thresholds

• Toxicity filters

• Bias & fairness audits

• Prompt validation rules

• Model drift detection

• Red teaming for AI

✅ Mandatory Explainability

SHAP/LIME for MLToken attribution, reasoning trace, and calibration for GenAI

✅ 5. Security, Compliance & Regulatory Controls

For BFSI, this is non-negotiable.

EA defines controls for:

• PCI-DSS, RBI, SEBI, IRDAI compliance for AI workloads

• Model output logging & auditability

• Encryption standards

• Secure access to LLMs (private endpoints, VNET integration)

• Data residency (no customer PII leaves India region)

• Prompt & response logging policies

Why:AI outputs must be auditable, safe, and regulator-ready.

✅ 6. Platform & Infrastructure Governance (AI Platform Engineering)

EA sets up a central AI Platform that all use cases can leverage.

Includes:

• Central vector database

• Model registry & feature store

• LLM orchestration layer (LangChain, Spring AI, Semantic Kernel)

• RAG pipelines

• Prompt templates library

• API gateway & throttling

• Monitoring dashboards (latency, accuracy, hallucination rate)

Why:Prevents each project from reinventing the wheel.

✅ 7. Delivery Governance (AI SDLC / LLMOps)

AI requires a modified SDLC.

EA defines:

• AI SDLC process with approval gates

• Versioning for prompts, embeddings, models

• Automated pipelines: training → testing → deployment

• CI/CD for AI (MLOps + LLMOps integrated)

• Change management for model updates

Why:Ensures reproducibility, traceability, and safe deployments.

✅ 8. People & Capability Governance

EA must establish:

• Role definitions: AI Architect, Prompt Engineer, Data Scientist, Model Reviewer

• Skills matrix and training roadmap

• CoE (Center of Excellence) for AI/ML & GenAI

• Best-practices community

• Standard documentation templates

Why:Scales AI beyond pilots into enterprise-wide programs.

✅ Summary Statement (Say This in Interview)

“To leverage AI, ML, and GenAI at enterprise scale, we need a strong EA Governance framework covering strategy, architecture standards, data governance, model governance, security, compliance, platform engineering, and delivery controls. I set up an 8-pillar governance model that ensures GenAI is secure, compliant, consistent, explainable, and continuously improved — while delivering measurable business outcomes.”