‘Software Will Keep Reinventing Itself’: Pismo’s Siva Ram Nyapathi

I am a Staff Software Engineer at Pismo, a Visa company, with more than 13 years of experience across software engineering, cloud platforms, enterprise modernisation, digital payments, logistics, and distributed systems.

My interest in computers began during my childhood in Nellore, at a time when computers were not yet part of mainstream school education. I started exploring them in the third standard and often spent time outside school trying to understand how they worked.

The first programming language I encountered was BASIC. Later, I became fascinated by websites and discovered Microsoft FrontPage, through which I began learning HTML. One of my earliest projects was creating a website for my extended family on Yahoo GeoCities.

That simple website sparked a deeper curiosity. I wanted to understand how a submit button worked and what happened behind the screen when someone interacted with a webpage. One question led to another, and I gradually explored Visual Basic, VBScript, FoxPro, databases, web development, and eventually enterprise application development.

What inspired me most was the ability of software to transform an idea into something useful for people. As the internet and technology evolved, I continued building applications, studying systems, and learning how different components worked together.

That curiosity has remained constant throughout my career. Even today, I enjoy going beyond the immediate requirement, understanding the larger problem, and designing solutions that are reliable, scalable, and meaningful to the people who use them.

What continues to inspire me about software engineering is its ability to convert systems thinking into measurable impact.

Software has accelerated progress across banking, healthcare, education, logistics, communication, and almost every other industry. A well-designed solution can save time, reduce errors, improve access, and positively affect thousands or even millions of people.

Whenever I observe a repetitive process or an operational challenge, I naturally begin analysing which parts can be simplified, automated, or handed over to a computer. That enables people to spend less time on routine work and focus more on decisions, creativity, and larger problems.

For me, software engineering is not only about writing code. It is about understanding a real problem, designing an effective solution, and improving it as the needs of users and businesses evolve. That combination of problem-solving, creativity, and impact is what drew me to the profession and continues to motivate me.

One of the most rewarding initiatives I worked on at Maersk was an industry-first touch-free delivery order solution within the import documentation ecosystem.

A delivery order is a critical part of the import journey because it enables customers to take possession of their cargo after it arrives. Delays, incomplete information, or processing errors at this stage can directly affect customers, port operations, and revenue. Our objective was to reduce manual intervention and create a more proactive, reliable, and scalable process.

I played an instrumental role in designing and developing the solution, modernising the supporting legacy systems, and improving the way data was ingested and exchanged across applications. The platform supported import operations across more than 20 countries, improved visibility, reduced opportunities for manual error, and enabled multiple delivery-order journeys that were important revenue-generating services for the organisation.

What made the initiative particularly rewarding was that it required much more than writing software. It demanded a detailed understanding of customer expectations, operational workflows, legacy dependencies, production risks, and the needs of the engineering teams responsible for maintaining the platform.

Beyond my primary product responsibilities, I introduced standard operating procedures for incident management, created rapid diagnostic guidelines, and developed tools to automate repetitive engineering activities. These tools improved developer productivity and significantly reduced the time required for routine tasks.

I also contributed to a complex infrastructure migration that was originally expected to take two to three months. By introducing automation, repeatable validation, and careful migration planning, we completed it in approximately three weeks without significant production disruption. The migration also helped save approximately USD 200,000 in infrastructure costs.

This experience reinforced my belief that meaningful engineering combines customer understanding, operational knowledge, automation, technical excellence, and measurable business value.

Agile and DevOps have fundamentally shaped the way I approach software development. Agile helps teams understand customer needs, break complex problems into manageable increments, and deliver value continuously. DevOps turns that mindset into an engineering practice by enabling teams to build, test, release, operate, and improve software with speed and discipline.

I began my career when development and operations were largely separate functions. Developers built applications, while another team handled deployment and production support. Over the years, I have seen that model evolve into automated pipelines, infrastructure as code, continuous delivery, observability, and shared ownership of production systems.

At Pismo, a Visa company, we work in a payments environment where frequent delivery must coexist with high standards of reliability, security, and control. That is possible only when engineering discipline is embedded throughout the development lifecycle.

My approach is guided by a few principles: document important decisions, write testable code, automate repeatable work, treat security as a foundational requirement, and run regression checks before releasing changes. Infrastructure configurations and database changes should also be version-controlled, reviewed, and deployed through reliable processes.

Speed should never mean bypassing governance. Strong approval mechanisms, observability, rollback strategies, and clear accountability are essential, especially in large-scale systems where a poor release can affect many customers.

After more than a decade in the industry, I have learned that Agile and DevOps are not merely tools, ceremonies, or deployment pipelines. They are cultures of feedback, ownership, collaboration, and continuous improvement that help organisations deliver faster without compromising stability, security, or customer trust.

My advice to aspiring software engineers is to build strong foundations before becoming distracted by every new technology or industry buzzword.

At the beginning of your career, focus on learning how to translate a client or business requirement into a working application. Spend time understanding the actual problem, the users affected by it, the expected outcome, and how the solution will be tested. Writing code is only one part of software engineering; delivering the right solution is what ultimately matters.

Develop practical proficiency in at least one compiled programming language, one scripting language, a simple user-interface framework, and both relational and non - relational databases. You should also be comfortable working with Linux, version control, APIs, debugging tools, and basic networking concepts.

Once you can independently develop and deliver a feature, gradually strengthen your understanding of data structures, algorithms, system design, performance, security, scalability, and reliability.

Practices such as CI/CD, DevOps, DevSecOps, cloud engineering, infrastructure as code, and automation are often understood more deeply when learned alongside real development work rather than only through broad standalone courses disconnected from practical application.

Start with small, meaningful increments: automate a build, add unit tests, create a deployment pipeline, containerise an application, introduce monitoring, or manage a small piece of infrastructure as code. When these practices solve a real problem in your own application, their value becomes easier to understand and retain.

Artificial intelligence should be approached in the same way—as a practical tool that complements strong engineering fundamentals rather than replacing them.

The technology industry evolves continuously, so engineers should aim to build a π-shaped skill set: deep expertise in two complementary areas, supported by a broad understanding of the surrounding technology landscape.

Most importantly, remain curious, learn continuously, accept feedback, and never lose sight of the people and problems your software is intended to serve.

At the end of the day, everyone working in technology is human. Our nervous system responds to perceived threats through the same fight-or-flight mechanisms that once helped our ancestors react to physical danger. Today, the threat may not be a tiger; it may be a critical P1 incident during a holiday, with systems failing, customers affected, and only one engineer available to respond. Yet the stress response can feel just as real.

Technology professionals work in an environment filled with uncertainty, constant change, complex problem-solving, production incidents, deadlines, and high expectations. Much of our work is cognitive rather than physical, which means we continuously place demands on our attention, judgement, and emotional resilience.

When this pressure continues without adequate rest or support, it can affect both individual well-being and team performance.

Unfortunately, workplace mental health is sometimes reduced to an occasional themed email, awareness campaign, or wellness session. While these initiatives are useful, meaningful support requires consistent attention. Leaders must create environments where people feel safe asking for help, discussing workload, admitting that they are overwhelmed, and taking time to recover without fear of judgement.

As an Accredited Mental Health First Aider, I believe early recognition and compassionate support can make a significant difference. We are not expected to diagnose or replace mental-health professionals. Our role is to listen without judgement, offer reassurance, recognise warning signs, and guide someone towards appropriate professional help when needed.

Healthy teams are not created only through better tools and processes. They are created when people feel supported, respected, and psychologically safe. When organisations take mental well-being seriously, employees are better able to think clearly, collaborate effectively, recover from difficult situations, and perform sustainably.

AI is often discussed primarily in the context of job displacement. While it will undoubtedly change certain roles and ways of working, I believe its more meaningful impact will come from helping organisations improve productivity, strengthen internal operations, and solve problems that were previously too time-consuming or complex.

We have seen similar concerns during every major technological transition. Computerisation changed clerical and operational work, the internet transformed communication and commerce, and cloud computing changed how organisations build and operate technology. Each transition disrupted existing roles, but it also created new industries, responsibilities, and opportunities. AI represents the next stage of that evolution.

The industry is still learning how to derive sustainable value from AI. Building and operating AI infrastructure is expensive, so organisations will need to move beyond experimentation and demonstrate measurable improvements in efficiency, quality, customer experience, or revenue.

In software engineering, the future is not simply about asking a general-purpose model to generate code. AI performs much better when it receives the right context, relevant data, clear constraints, and access to carefully designed tools. This context must be built progressively through documentation, source code, architecture information, operational data, and feedback.

I see effective AI systems as a combination of large language models, specialised agents, and focused utilities written in conventional programming languages. An orchestration layer can coordinate these components, while standards such as the Model Context Protocol can help AI systems connect consistently with tools, applications, and data sources.

Human presence, however, must remain central. AI should operate within appropriate guardrails, access controls, validation mechanisms, audit trails, and approval processes. The level of human oversight should increase with the potential impact of a decision. AI may assist with analysis and recommendations, but accountability, ethical judgement, security, and critical decisions must remain with people.

AI is here to stay. Engineers should become comfortable using it in everyday work, automate repetitive tasks where appropriate, and use the time gained to solve larger problems that require creativity, domain understanding, and human judgement.

My long-term professional goal is to grow into an Enterprise Solution Architect who can design scalable, reliable, and secure systems while keeping practicality and profitability at the centre of every decision.

Architecture is often presented through diagrams, frameworks, and whiteboard discussions. However, a successful architecture must work in the real world. It should solve the customer’s problem, support operational teams, remain manageable for engineers, and deliver measurable value to the organisation. A technically impressive solution that is too expensive, difficult to operate, or unnecessarily complex is not necessarily a good solution.

To prepare for this role, I want to continue strengthening my knowledge across distributed systems, cloud-native platforms, security, observability, DevOps, artificial intelligence, and enterprise integration. At the same time, I want to deepen my understanding of business strategy, operational constraints, cost optimisation, and how technology investments contribute to revenue and long-term sustainability.

Good architects must listen closely to customers, business stakeholders, operations teams, and the engineers who will build and maintain the solution. Architecture should emerge from this collective understanding rather than being imposed from a distance.

Technology changes rapidly. Programming languages, cloud platforms, frameworks, and tools will continue to evolve. My goal is not simply to become an expert in one technology, but to develop strong architectural principles that can be applied across changing technology landscapes.

The legacy I hope to leave is a practical engineering culture built on simplicity, automation, reliability, security, reusability, and continuous improvement. I also want to mentor engineers to look beyond individual features and understand the wider customer, system, operational, and business impact of their decisions.

Ultimately, I want to be known as an architect who designed systems that were not only scalable, but also valuable, sustainable, operable, and commercially sensible.