Q1. You’ve led large engineering and delivery organizations across multiple geographies — could you briefly outline how your responsibilities have evolved as you moved from hands-on development into transformational engineering leadership?

I began my career as a hands-on engineer, working primarily with C#.NET and Java, where I was responsible for delivering individual components inside intricate, multi-application environments. As my scope expanded at TCS and later at Fiserv, I transitioned into program management roles. In these positions, I led teams of more than 140 people, managed delivery risks, and ensured consistent, reliable outcomes for business-critical platforms.

My roles at FIS and EXL represented a clear shift from day-to-day operations to leading transformational change. During this phase, I focused on:

• Leading cloud-native and microservices initiatives that delivered 30% enhancements in system performance
• Overseeing products generating $100 million in revenue, with direct engagement at the executive leadership level
• Building and scaling teams rapidly, including growing the NIUM engineering team from zero to 25 members in under four months while maintaining 99.9% system availability

Today, my leadership approach is focused on strategic synchronization. This includes making well-informed build-versus-buy decisions, ensuring engineering roadmaps directly support go-to-market strategies, embedding governance, and driving a metrics-led culture. These actions have resulted in to reducing attrition from 18% to 6% while sustaining delivery velocity.

 

Q2. As GenAI accelerates coding, testing, and problem-solving workflows, what shifts do you see as most critical for engineering teams to realize true AI-augmented productivity rather than incremental tooling gains?

In my experience, the real value of GenAI lies in rethinking how work is structured and delivered, not in simply layering new tools onto existing processes.

During GenAI pilots at FIS and EXL, we applied AI across delivery reporting, risk and dependency identification, and knowledge management. This resulted in approximately 20% productivity gains without jeopardizing quality. Based on this experience, I see three critical shifts:

• From experimentation to governance: Establishing ethical AI frameworks that tackle data lineage, bias controls, and auditability
• From skills to capabilities: Upskilling teams in prompt engineering and effective human–AI collaboration, not just model usage
• From vanity metrics to business outcomes: Measuring impact through cycle time, defect leakage, and throughput, with a target of 20–30% efficiency gains

The most common challenge I have observed is when GenAI projects persist isolated pilots rather than becoming part of core delivery processes. Sustainable value is achieved only when GenAI is embedded into DevOps pipelines and day-to-day business processes.

 

Q3. Enterprises continue to modernize legacy platforms into microservices and cloud-native architectures. From your experience, what are the biggest execution pitfalls teams face?

From my experience, modernization efforts commonly encounter difficulties not because of technology limitations, but because the complexity of transformation is underestimated. The most frequent traps I have encountered include:

• Data gravity blindness: Migrating monoliths without rethinking data models or persistence strategies (such as SQL to NoSQL or polyglot approaches), frequently causing to 20–30% performance regressions
• Resilience gaps: Insufficient focus regarding chaos engineering, autoscaling, and failover testing in Kubernetes or AKS environments
• Architectural debt: Creating distributed monoliths by overlooking domain-driven design and observability

At FIS, our legacy-to-cloud modernization was successful because we emphasized iterative proofs of concept, shift-left security using tools such as Checkmarx and Veracode, and full-stack observability with Splunk and PowerBI. This approach delivered a 30% performance improvement, 20% efficiency gains, $200K in cost savings, and a 25% reduction in project schedules.

 

Q4. SAFe and scaled agile models are widely adopted, yet many organizations struggle with alignment and predictability. What practices have you found most effective?

In my view, SAFe delivers value when it creates alignment without jeopardizing speed.

The synchronization practices I have implemented successfully include:

• ART-level PI planning supported using shared JIRA and Rally backlogs to secure transparency
• Daily cross-team standups and swarm models to resolve blockers within 24 hours
• Metrics-driven governance that tracks velocity, predictability, and escaped defects through lightweight steering forums

At Fiserv and FIS, these practices enabled 20% faster release cycles while supporting programs of over 110 people across data, reconciliation, and payments platforms.

 

Q5. With rising expectations around reliability and faster release cycles, how should engineering leaders evolve their DevOps, SRE, and DevSecOps approaches?

I have found that giving precedence to speed without preserving consistency introduces unacceptable risk. The most effective approach is an integrated DevOps, SRE, and DevSecOps model.

The principles I consistently advocate include:

• Security as Code: Embedding static and dynamic scans directly into CI/CD pipelines, improving code quality by approximately 50%
• Reliability engineering: Defining SLIs, SLOs, and error budgets, enabling 99.9% uptime as achieved at NIUM
• FinOps automation: Using Terraform and Harness to continuously optimize cloud costs
• Platform engineering: Implementing self-service developer platforms such as Backstage, decreasing average handling time by 20% and manual errors by 20%

In regulated industries like banking, zero-trust security and chaos engineering are no not optional anymore—they are essential.

 

Q6. Global delivery setups demand unified culture and consistent quality. What approaches have worked for you throughout distributed teams?

In my experience, building a successful global delivery organization requires a deliberate and sustained focus on culture.

Across teams in Pune, the US, and the UK, we maintained engagement and clarity through:

• Weekly all-hands sessions and asynchronous alignment using ServiceNow-based RACI models
• Cross-training initiatives, internal mobility programs, and hackathons to strengthen cultural bonds
• Continuous listening via Glint pulse surveys, which improved engagement scores to 95+

These efforts reduced attrition from 18% to 6%, increased productivity by 20%, and enabled smooth scaling—from early-stage teams at NIUM to delivery units of more than 50 people at EXL—without losing accountability or morale.

 

Q7. If advising senior leadership or investors, where would you prioritize technology investment over the next 3–5 years?

For enterprises, particularly in banking and fintech, my investment priorities are clear:

• AI-Augmented Modernization: Combining GenAI with cloud-native architectures to achieve 30%+ speed and effectiveness gains
• Reliable Digital Platforms: Investing in SRE, DevSecOps, and regulatory-grade reliability as a source of competitive differentiation
• Engineering Automation: Using agentic AI across testing, operations, and support to accelerate go-to-market timelines by 25%

My fundamental principle is to avoid investing in technology for its own sake. Instead, I recommend focusing on composable, outcome-driven architectures that support large-scale platforms, deliver measurable cost savings, and scale reliably.

To conclude, modern engineering leadership is no longer about managing delivery—it is about orchestrating ecosystems of people, platforms, and intelligence to create durable business advantage.