Role Of Engineers In Product Development

What roles turn a legacy technical team into an AI team that’s ready to deliver value vs. endless PoCs? Just as the AI stack must prioritize value over hype, the AI team’s composition must realign to deliver growth. Data analysts make excellent decision analysts. The focus moves from reporting (BI) with no value to outcomes (AI) with high business and customer impact. Why do business users need data? What outcome or customer value are they trying to deliver? The transition to decision analytics puts the data analyst’s technical skills in line with their business and domain expertise. The result is a high-value role. Data and BI engineers are in the best position to support the business’s emerging information needs. High-value AI is an information product. Decision-makers need information to improve outcomes and create value more efficiently. ML engineers and data scientists have AI engineering skills, so the major shift happening here is from PoCs to products. The product-first mindset and skillset are critical to support AI teams that directly impact the top and bottom line. Product owners and PMs are becoming product strategists and value owners. They ensure that the AI team only works on projects with significant ROI. They shield the AI team from endless PoCs by supporting opportunity discovery and enforcing value-centric prioritization. AI is fundamentally different from prior technologies, so it requires new capabilities and roles. AI Platform Engineers: AI isn’t a standalone technology, so a multi-technology platform is crucial. Agentic Workflow Engineers: Workflows must be reengineered for AI to deliver value. Bolt-on AI doesn’t deliver enough value to justify the costs. Hardware Optimization Engineers: Keeping training and inference costs low is a massive competitive advantage. It makes more use cases economically feasible and delivers higher margins. AI Ops Engineers: AI in production requires constant attention and modification to ensure reliable operation. AI Evaluation & Quality Engineers: Reliability is another massive competitive advantage. AI must work within specific guarantees, or customers won’t pay for it, and internal users won’t adopt it. What roles am I missing (I left one out on purpose)? What is your business doing to transition its legacy technical teams into value-centric AI teams?

Microsoft just put $2.5B and 6,000 engineers a new business unit, and Amazon Web Services (AWS) answered with a $1B forward‑deployed engineering unit. Here's why. Microsoft and AWS separately are going to use Forward Deployed Engineers (FDEs) that will embed with customers to build production AI systems. ------------------- So what are these forward deployed engineers? Forward deployed engineers are software and AI engineers who sit with the business, own outcomes, and make sure critical systems actually work in the messy reality of a customer’s environment. * Own the full deployment lifecycle: discovery, scoping, system design, implementation, integration, rollout, and measurement of impact. * Embed with customer teams: join their stand‑ups, Slack, and even offices to deeply understand processes, constraints, and politics. * Build real software: full‑stack systems, data pipelines, configurations and automations on top of an existing platform or model stack. * Bridge product and field: take sharp feedback from deployments back into core product and model roadmaps, so learnings are codified into reusable features rather than one‑off solutions. * Drive adoption and change: train users, socialize benefits, handle compliance questions, and make sure the thing is trusted and used, not shelved. If a traditional engineer asks “what can I build with this platform?”, an FDE asks “what must we build so this customer gets a measurable outcome in their environment?”. So you need to put your engineers in an upskilling program like Palantir Technologies does to learn how to become FDEs. ------------------- Why it matters The role is exploding because the bottleneck moved from “do we have a powerful model?” to “can we safely wire it into our business, at scale, across org and regulatory constraints?”. AI pilots die in the gap between a demo and a compliant, integrated, maintained system; FDEs are explicitly designed to close that gap. As a business leader, you need to develop a small internal squad of forward‑deployed engineers, rotating through your highest‑value use cases. They can be a mixture of vendor provided FDEs and your own. This will do more for AI adoption than your next innovation summit. If you want to learn more, check out Vinoo Ganesh's Linkedin course and materials. ------------------- ♥️ Dr. Ayesha Khanna, an Enterprise AI Operator who cuts through the hype and focuses on what actually works in business.

Are your engineers just building cool stuff or solving customer problems? I had a fascinating conversation with Matt Watson, founder and CEO of Full Scale, for last week's Product Thinking Podcast. Matt shared something that many developers, engineers and even tech leaders need to hear. "If you're not focusing outwards on the customer, you're focusing inwards, which is just basically on the code. You're like, my code is my product, but it's not. Nobody cares about your code. They care about what your code does." This resonated because I see this everywhere. Engineering teams get trapped in what Matt calls "inward thinking." They're obsessed with the elegance of their code, the complexity of their architecture, the satisfaction of closing Jira tickets. It's like playing with Legos all day → fun for the builder, but meaningless to the customer. The shift to "outward thinking" changes everything. Teams start focusing on why features matter to customers rather than just how to build them. They celebrate customer impact rather than shipped code. This isn't about micromanaging engineers or dismissing technical excellence. It's about aligning everyone around the same north star: customer value. How are you helping your engineering teams make this shift from inward to outward thinking?

Beyond getting the code to work, a developer should step into the shoes of other roles to bridge the gap between 'Code and Customer value'.   Think like a, 🤔    👉 Product : 🔹 How is this feature helping users ? Is it solving a pain point ? 🔹 What is the impact it brings in - Is it engagement, retention or conversions ? 🔹 What success metrics looks like ? What are the measurable KPI's ? 🔹 Do we have the right instrumentation for measuring it in production ? 🔹 What is your A/B strategy ?    👉 Designer : 🔹 Is the design intuitive enough ? 🔹 Is it visually appealing to the user ? 🔹 Does it simplify or complicate the user journey ? 🔹 Are you using patterns that User are already familiar with ?    👉 QA Engineer : 🔹 What are all the edge cases beyond happy flows ? 🔹 How am I gracefully handling on all the errors, timeouts & failures ? 🔹 What is the impact to customer under high load ? 🔹 Is the experience same across different devices or network conditions ?   Most importantly,    👉 Be your own Customer : 🔹 Is the feature intuitive and straight forward to use ? 🔹 Are there any unnecessary steps, delays or friction ? 🔹 Is it Fast & Responsive ? 🔹 Is navigating from one screen to another seamless ? 🔹 Is data parity maintained throughout the App ? 🔹 Are the messages or nudges you see are clear and concise, but not too overwhelming ?   This mindset ensures that every feature not only functions correctly but also delivers a compelling user experience in the products we build. 🚀🚀   #tech #careergrowth #myntra

Knowledge vs Experience In every professional journey — especially in the oil & gas industry — there comes a point where we realize that knowledge and experience are not the same thing. Knowledge is what we learn. Experience is what we survive. You can read hundreds of manuals, attend training after training, and know every theory about artificial lift systems, pressure curves, and production optimization. That’s knowledge — and it’s powerful. But experience… that’s different. Experience is what happens when theory meets reality — when you’re standing at the wellsite at 2 a.m., facing an unexpected shutdown, and you have to decide quickly what really matters. 📘 Knowledge tells you how the pump should behave. 🧠 Experience tells you what to do when it doesn’t. 📘 Knowledge gives you equations and guidelines. 🧠 Experience gives you instincts, timing, and judgment. At PT. Endurance Lift Dynamics Indonesia, we live in the balance between both. Because innovation without knowledge is guesswork — but knowledge without experience is just paperwork. When we introduce new lift technology, controllers, or gas separation systems, we rely on data, design, and simulations. But what makes the difference in the field isn’t just the specs — it’s how our engineers and partners have experienced real challenges and learned what truly works under pressure. One day, you might meet a young engineer with deep theoretical knowledge but limited exposure. Another day, you’ll meet a field technician who’s been in the industry for 25 years but never studied the latest algorithms. Both are valuable — but the magic happens when they work together. Because knowledge feeds innovation, and experience grounds it in reality. I’ve seen projects succeed not because the technology was the most advanced, but because the team had the humility to listen to experience — and the curiosity to keep learning. In this industry, every failure teaches more than a successful trial. Every broken rod, every unexpected shutdown, every late-night restart — they all turn knowledge into wisdom. So, if you’re new in your career: 👉 Don’t rush to prove that you know everything. Seek opportunities to experience it. And if you’ve been in the field for years: 👉 Don’t stop learning. The industry is changing fast, and your experience becomes even more powerful when paired with updated knowledge. True expertise is not about choosing between knowledge or experience — it’s about combining both until they become insight. That’s what drives progress. That’s what builds trust. And that’s what keeps our wells — and our careers — running strong. #Leadership #OilAndGas #Engineering #KnowledgeVsExperience #EnduranceLiftDynamics #ProfessionalGrowth #ArtificialLift #EnergyIndustry #Teamwork #ContinuousLearning #FieldExperience #Mentorship #Innovation

Your engineers should annoy your PMs (I say this now as a product leader). As a senior product manager, I launched a new onboarding flow that boosted trial conversions by 25%. I was riding high on success…and then an engineer on my team suggested removing an extra step. Sure, it would further reduce cognitive load and drive even more conversions, but I was so in love with the design of the onboarding flow that I got annoyed. I pushed back—despite clear data! In the end, that engineer (rightfully) sidestepped me, ran the experiment, and proved me wrong. My self-indulgence almost cost our team another win. Now, as a product leader, I can see that customer- and data-centric engineers who help define product are a product manager’s gold mine. They enable PMs to scale out of the day-to-day and drive more impact across the organization. Leading tech companies and high-growth startups already encourage engineers to act like PMs. With AI making customer, competitor, and market insights more accessible, product-centric engineering will soon be standard everywhere. Product leaders, here’s how to embrace and empower these cross-functional teams: 1️⃣ Foster a culture where engineers, designers, and customer success teams don’t just share ideas, but actively shape product definitions. This allows PMs to act like air traffic controllers rather than pilots, guiding multiple “flights” simultaneously so more initiatives can land successfully. 2️⃣ Provide the right tools broadly—including direct access to customer feedback and data—so every role can make informed recommendations. 3️⃣ Encourage PMs to delegate and scale beyond their core responsibilities, taking on broader, more cross-functional work while peers step up with product insights. Your PMs and your organization as a whole will benefit. How have you encouraged your PMs to scale themselves? #productmanagement #productleadership

Some of the best lessons I have learned in my career came from very expensive outages. In 2010, Amazon went down for 10 minutes because of my mistake. No engineer wants production to go down, nor do they look forward to that kind of stress. But when something breaks in production, you get to see the system tell you the truth. In my case, one of those lessons came the hard way. In 2010, amazon went down for 10 minutes because of my mistake. It was painful, expensive. And I can still remember it because incidents like that burn the lesson into you in a way no happy-path project ever can. (I'll link the story in the comments) Over nearly two decades in tech, across Amazon, Paytm, Tokopedia, and now 5 years at Google, I have learned a lot from production incidents, outages, and the postmortems that followed them. That is why I understand what Arpit is really saying here. He is excited because when everything breaks, engineers get a great chance to learn for the better. That is the mindset younger engineers should build. Read RCAs. Read outage reports. Read postmortems carefully. Look at what changed afterward. Which guardrail got added? Which process got tightened? Which assumption turned out to be wrong? That is where a lot of learning is. If you work with AI systems, or if you work at a company that is scaling fast, you are operating in environments where small mistakes can become large incidents very quickly. Supply chain attacks, cascading failures, bad rollouts, silent data issues, model regressions. The blast radius gets bigger as the systems get smarter and more connected. So yes, incidents are bad for business. But for engineers who are serious about growing, incidents are also some of the best classrooms they will ever get.

After reviewing 300+ AI/ML resumes, here's what actually gets people hired 👇 (I'm an AI/ML Engineering Manager and these patterns separate successful candidates from those who struggle) 🔹 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀𝗻'𝘁 𝘄𝗼𝗿𝗸 𝗮𝗻𝘆𝗺𝗼𝗿𝗲: → 15 toy projects with MNIST datasets → Memorizing every new AI paper without implementation → Perfect theoretical knowledge but can't deploy anything → Chasing every framework instead of mastering fundamentals → Building "Hello World" chatbots and calling it RAG 🔹 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗴𝗲𝘁𝘀 𝘆𝗼𝘂 𝗵𝗶𝗿𝗲𝗱: 𝗦𝗼𝗹𝘃𝗲 𝗿𝗲𝗮𝗹 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 → Build a document Q&A system for legal contracts → Create a resume screening bot that actually works in production → Design systems that handle edge cases and failures gracefully 𝗘𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 → Show you can take messy data → clean model → production deployment → monitoring → Demonstrate cost-latency tradeoffs, not just accuracy metrics → Prove you understand when NOT to use LLMs 𝗦𝘁𝗿𝗼𝗻𝗴 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 + 𝗖𝗼𝗱𝗶𝗻𝗴 𝘀𝗸𝗶𝗹𝗹𝘀 → I'd rather hire someone who deeply understands linear regression than someone who can name 50 algorithms → Can you implement cosine similarity from scratch? → Debug a broken RAG pipeline? 🔹 𝗥𝗲𝗱 𝗳𝗹𝗮𝗴𝘀 𝗜 𝘀𝗲𝗲 𝗼𝗳𝘁𝗲𝗻: 𝗙𝗼𝗿 𝗝𝘂𝗻𝗶𝗼𝗿 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀: → Can't explain their own projects clearly → No deployed demos or GitHub links → Theory without practical application → Weak coding fundamentals 𝗙𝗼𝗿 𝗦𝗲𝗻𝗶𝗼𝗿 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀: → No system design thinking or scalability awareness → Can't discuss cost-performance tradeoffs → Haven't dealt with real production issues → Focus on trends over engineering judgment 🔹 𝗠𝘆 𝗮𝗱𝘃𝗶𝗰𝗲 𝗯𝘆 𝗹𝗲𝘃𝗲𝗹: 𝗝𝘂𝗻𝗶𝗼𝗿 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 (𝟬-𝟮 𝘆𝗲𝗮𝗿𝘀): → Focus on 80/20: Coding fundamentals, 2-3 deployed projects, basic system design → Don't try to master everything - depth beats breadth → Build something users actually interact with 𝗠𝗶𝗱-𝗹𝗲𝘃𝗲𝗹 (𝟮-𝟱 𝘆𝗲𝗮𝗿𝘀): → Show you can own features end-to-end → Demonstrate debugging skills and production experience → Understand when to use which tools and why 𝗦𝗲𝗻𝗶𝗼𝗿+ (𝟱+ 𝘆𝗲𝗮𝗿𝘀): → We're looking for engineering judgment and system thinking → Can you design systems that scale and handle edge cases? → Do you mentor others and drive technical decisions? The reality: The bar is higher than ever, but opportunities are massive for those who prepare strategically. Consistency beats intensity - daily practice with real problems trumps cramming. 💭 Comment your thoughts below 👇 -- ♻️ Repost if you think its helpful 👍 ➕ Follow me, Shantanu for production AI/ML/MLOps & careers ➕ Join 21.000+ AI/ML builders here: https://lnkd.in/ds_SzEUH

The best engineer I’ve ever worked with wasn’t flashy. He didn’t chase big wins or impressive demo moments. What made him great was something much quieter. We were building a major new part of the platform. In the middle of it, a tiny issue kept popping up. But it was a true edge case. One in ten thousand. Barely noticeable. And definitely non-catastropic. I told him to ignore it and move on. He said okay… and then disappeared for a few days. When he came back, he had fully isolated the issue. He knew exactly how it happened, and why it happened. Plus he had already fixed it. That fix didn’t move a metric that week…or that month. But here’s what it did do over time: Those tiny edge cases compound. If you let enough of them slide, users eventually run into them. And when they do, trust erodes. Slowly. Quietly. Invisibly. If you take care of them – even when it feels inefficient in the moment – something else compounds instead: Reliability.  Confidence.  Trust in the product. Great engineers don’t just ship features. They refuse to let small cracks turn into structural ones.

If I had to pick a single litmus test for a strong product culture, it would be this: Are your product teams consistently prioritizing solution ideas that came from engineers? Marty Cagan has called an empowered engineer “the most important thing” for a team that wants to innovate consistently: engineers know better than anyone else what’s just now possible with technology. I'd add that the existence of empowered engineers is also *the* best diagnostic for whether you’ve fully embraced the product operating model.  Why? Because you don't get empowered engineers unless you're doing pretty much everything else right. Your product leaders have created, and effectively communicated, the strategic context (vision + strategy + OKRs) that both inspires the team and focuses them on the most important problems.  Nobody is dictating a roadmap: the team is given problems to solve, not features to build. Your PMs and designers are truly collaborating with engineers in discovery. And your engineers understand your customers and users at a deep level. The mindset shift, and the corresponding changes in habits, are often significant. It means expecting and enabling engineers not just to deliver quality code with velocity, but also to act as key partners in figuring out what to build in the first place. If I had to recommend one thing to catalyze this change: get at least one engineer into your customer discovery conversations. I've worked with several clients to implement this small step, which can literally happen overnight — invite the engineer! In one case, we had a senior engineer who'd been in order-taker mode join customer calls alongside the PM and designer. The shift was immediate: motivation changed, ideas started flowing, and that engineer went from executing specs to truly collaborating as a trio with his PM and Designer. As long as you've chosen an engineer who's curious about customers — and sometimes even if they're skeptical at first — I've yet to see this fail to shift the dynamic. Having an engineer in customer conversations does several things at once: • It signals that you *want* engineers to be empowered. Otherwise, why would you be spending their valuable time talking to customers versus writing code? • It dramatically accelerates their understanding of, and empathy for, customer problems. When problems are experienced firsthand, solution ideas come fast. • Sprint planning shifts from "we're skeptical about what product is telling us to build" to "we know these priorities were informed by, if not inspired by, an engineer." • Specs get better — since engineering has been involved upstream — which speeds up delivery. What's your litmus test for a strong product culture?