4 Adapting Engineering Skills to New Trends and Technologies
In today's rapidly evolving technological landscape, engineers face the challenge of staying ahead of the curve. This article delves into cutting-edge trends and technologies that are reshaping the engineering field, from AI integration to cloud cost management. Drawing on insights from industry experts, readers will discover practical strategies for adapting their skills and embracing these transformative changes.
- Embrace AI as a Powerful Engineering Tool
- Apply Industrial Engineering Across Software Development
- Adapt Monitoring Platform for Cloud Cost Tracking
- Transition from REST APIs to GraphQL
Embrace AI as a Powerful Engineering Tool
Absolutely. Let me give it to you straight.
A few years ago, adapting your engineering skills meant learning a new framework or picking up a better linter. Today? It means learning to work with machines that can code. And if that doesn't light a fire under you, you're not paying attention.
Here's my example: When AI coding assistants like Copilot started making noise, I didn't dismiss it--I dove in. Everyone was debating whether it would "replace developers," but I saw it for what it was: a seismic shift. I rewired my workflow to treat AI like a junior engineer that never sleeps--feeding it structured prompts, iterating on its outputs, and letting it handle the tedious stuff like boilerplate, test generation, and repetitive queries. Suddenly, I wasn't just writing code--I was orchestrating it.
I've been a backend engineer, a data engineer, and a DevOps lead. Across every role, I've seen this truth emerge: AI won't replace you--but engineers who wield AI will absolutely replace those who don't. QA? Mostly automatable. DevOps? AI is handling incident response better than most juniors ever could. Data pipelines? I've had AI generate transformations in seconds that used to take hours. And that's just the beginning.
Adapting to AI isn't about survival--it's about leveling up. I've shifted my value from being a code monkey to being a system thinker. From just knowing the syntax to knowing how to guide the machine. And honestly? It's made me 10x more effective.
So when people ask if I've had to adapt to a trend? Hell yes--and not just adapt. I leaned into it hard. Because the engineers who treat AI like a threat are already being left behind. The ones who treat it like leverage? We're building the future faster than ever.
Adapt or fade. That's the game now.
Apply Industrial Engineering Across Software Development
I am an Industrial Engineer by qualification; however, I have never worked in the manufacturing sector applying those skills.
Throughout my career, I have been in software development, starting my career at TCS in 1986. My first project was to deliver COBOL software to manage the sales, purchase, inventory, and financial business processes for food chemical manufacturing.
Later, I also worked in China in Business Process Outsourcing - digitization of large volumes of documents to automate 'Data Capture'.
Now at IRESC, as a Product Manager, I design and develop a Low Code platform to manage HSE risks as well as ESG reporting.
In all these "Product" development (SDLC) or "Business Process Improvement" projects, I had the opportunity to apply my Industrial Engineering skills - Plan Do Check Act (PDCA). Most businesses have one workflow or another. The engineering concept of measuring Key Performance Indicators comes in handy.
While science begins with asking questions, engineering begins with defining a problem to solve. The goal of science is to construct explanations for phenomena. The goal of engineering is to solve problems.
I strongly believe in the Industrial Engineering philosophy of "What Cannot be Measured Cannot be Controlled" - only when senior leaders can have visibility of performance, bottlenecks, and root causes can they take effective measures to improve.
Today, businesses cannot survive without learning how to apply Artificial Intelligence and Machine Learning (AI/ML). Though there are many hypes, with proper knowledge and skills, one can scale multiple fold.
In the past few years, I was able to do the marketing of our product Haz360 to the target segment only manually, trying to connect leads via LinkedIn and messaging them manually (copy, paste, edit) and follow up manually (many times missed due to other pressing issues). With limited resources, I wish I could clone and replicate myself 24 hours a day like in the 1996 movie 'Multiplicity'.
But with AI/ML, I am now moving towards increasing my outreach by applying AI-automated workflows, increasing multiple fold (message templates, personalization, video creation, etc.). Combined with engineering skills and curiosity to learn, I believe I am able to follow the new trends and technology.
It is always a debate whether sales is a numbers game or a skills game. Personally, I believe it is both. First, you need to increase topline numbers, then by measuring and applying better techniques, you can improve results.
Adapt Monitoring Platform for Cloud Cost Tracking
As the team members of a website monitoring and observability platform, we knew staying ahead of the curve was crucial. When cloud adoption boomed, we foresaw the need for cost optimization alongside performance insights. We pivoted our tool to integrate cloud cost tracking, allowing users to pinpoint inefficiencies. We also prioritized clear visualizations within our platform, fostering collaboration between developers and operations. By adapting to the cloud landscape and prioritizing user needs, we empowered teams to gain observability and optimize their cloud spend.
This shift wasn't easy. We actively sought feedback from beta testers using cloud-based deployments, ensuring our tool seamlessly integrated with existing workflows. We are still updating our tool as we recently launched the latest version of Middleware. This collaborative approach not only kept our team current but also resulted in a more robust and user-friendly platform that addressed the evolving needs of cloud users.
Transition from REST APIs to GraphQL
One that stands out was when we transitioned from traditional REST APIs to GraphQL on a client project that needed faster iteration and more flexible data fetching. At first, it felt like flipping the entire mindset upside down—no more rigid endpoints; clients could ask for exactly what they needed. Cool, but also kind of chaotic if not done right.
So I dove into it. I took a week, built a sandbox app, played with Apollo Server and Client, and refactored one microservice as a test. What clicked was realizing GraphQL wasn't just a new tech—it was a different way of thinking about the relationship between frontend and backend. Once we integrated it fully, our API response sizes dropped, development velocity went up, and onboarding new features got smoother.
The real lesson here is to stay curious and hands-on. Don't wait for full mastery—just start building. That's how you adapt without stalling.
