Tech Industry Job Roles

The product trio is merging. And it's not just because of AI. In fact, I first saw this concept in 2020, presented by Yuhki Yamashita, CPO of Figma. (ie, well before the current AI boom) — It's driven by a few key trends: 1. Engineers are taking an active role in the problem space The early startup paradigm of product-focused engineers is moving into larger companies. It's just a pre-requisite for engineering success these days. They don't just ask, how big was the feature, or important the technical innovation. They ask: how big was the impact? Naturally, tech leads and engineering managers everywhere have become more critical of driving that. 2. Designers are taking an active role in the business More than ever, designers are learning and building for metrics. In a world where OKRs rule the roost, the incentive is natural. They can't just trust a PM to be able to fully vet the business space. Because, too many times, the PM gets it wrong. And this creates an environment where collaboration tends to do better than silos. 3. PMs are learning design and tech drive performance The little details of how you design a product and the technical decisions you make determine whether it's successful. Both product leaders and product managers are realizing they have to get into the details. You can't just have a conversation with a CEO who is in the details of the product by outsourcing it all to your tech and design partners. 4. Finally, yes, AI is accelerating the merge Now: • Design engineer is the hottest thing on Twitter • Companies everywhere are waiting to hire PMs • All disciplines continue to use AI to speed up their work This will reduce time on finessing details & increase time on the why. — Putting all this together... It seems undeniable the roles have started to merge on the edges. But, the core responsibilities remain differentiated. So, in this increasingly overlapped world, how you work with your sister functions becomes a differentiator. Those who: • Lead with empowerment • Collaborate with empathy   • Blend roles, but don't step on toes Will be the one's leading us into this new era. On the other hand, those that try to maintain the silos, will find themselves outdated.

Product Manager (PM), Product Owner (PO), and Business Analyst (BA): Who Does What? Alright, let’s start with my role—Product Manager. If you’re in product, you know the roles of Product Manager, Product Owner, and Business Analyst sometimes overlap, sometimes blur, and sometimes look completely different. Here’s how these roles break down, why each matters, and what it means for your team when you get it right. Product Manager (PM) Scope: Owns the product strategy, vision, and long-term success. Decision-Making: Makes all the strategic calls around why the product exists and where it’s headed. Deliverable: Revenue, adoption, and overall product success. As a PM, I’m always looking to understand our market, stay ahead of trends, and ensure that our product doesn’t just meet user needs but drives real value and growth. I make sure the product vision stays sharp, the goals stay ambitious, and the strategy makes sense for the business. Product Owner (PO) Scope: Manages the product backlog and prioritizes features so the development team stays laser-focused. Decision-Making: Owns tactical, feature-level priorities—deciding what gets built when to deliver the most value. Deliverable: Sprint and release success. A well-maintained backlog with clear priorities. The PO is the critical bridge between dev and business, making day-to-day calls so the team delivers the right stuff, on time, with minimal blockers. They know the product inside and out at the feature level, and they ensure the development team is aligned and productive each sprint. Business Analyst (BA) Scope: Dives deep into requirements, aligns stakeholders, and documents what’s needed. Decision-Making: Makes recommendations—but doesn’t typically own decisions. Deliverable: Accurate, complete requirements that ensure nothing falls through the cracks. A good BA makes sure every possible scenario is considered and documented, which is essential for quality builds. They’re experts in asking the “what-ifs” and making sure we all have a shared understanding before we get into development. The Key Differences PM → Thinks big-picture, sets the vision, and drives product-market fit. PO → Prioritizes tactically, bridges dev and business needs during sprints. BA → Details-oriented, ensures alignment on requirements. The Reality After working across all three roles, I can say: it’s not always this neat. In a given week, you might end up wearing 2 or 3 of these hats, especially in lean teams. But knowing these distinctions helps drive smoother collaboration, faster progress, and better results for your product. If you’re in a smaller setup where one person handles multiple roles, here’s a tip: be clear about your hat at any given time. It’ll save you and your team a lot of confusion and help keep projects on track. Bottom line? When PMs, POs, and BAs each know their roles—and respect each other’s strengths—the product wins.

Ever wondered how a real AI project actually works ? A successful AI project goes through 7 structured steps, each led by different experts. From defining the business problem to continuous improvement after deployment, every role plays a part in making AI work in the real world. Here’s a cheat sheet that breaks down the end-to-end AI project lifecycle with clear steps, leaders, and responsibilities. ✅ AI Project Steps Covered: 🔹Step 1: Defining the Problem → Led by business analysts & product managers. Identify real problems, set objectives, align business & tech needs. 🔹Step 2: Preparing the Data → Led by data engineers & analysts. Collect raw data, clean, standardize, and split into training, validation, and test sets. 🔹Step 3: Building the Model → Led by ML engineers & data scientists. Choose algorithms, engineer features, train models, tune hyperparameters, and compare best fits. 🔹Step 4: Testing & Evaluation → Led by data scientists & ML researchers. Validate with unseen data, use metrics (accuracy, recall, AUC), stress-test, and decide if model is production-ready. 🔹Step 5: Deployment → Led by MLOps engineers & software developers. Package models into APIs, use Docker/Kubernetes, integrate with apps, enable predictions, and ensure reliability before going live. 🔹Step 6: Validation & Monitoring → Led by validators, ethicists, QA teams. Monitor accuracy, detect drift, check bias, log failures, and trigger alerts if performance drops. 🔹Step 7: Continuous Improvement → Led by data scientists, PMs, domain experts. Gather feedback, add new data sources, retrain, optimize pipelines, and push regular updates. Save this guide and share with others, and hopefully this will help to understand how AI projects work, step by step, role by role! #AI

AI-ML Lifecycle and Key Job Roles   In this age where we see more and more potential of how artificial intelligence and machine learning (AI/ML) can revolutionize industries and processes. Hence it is critical that everyone be aware of the AI-ML lifecycle and the job roles associated with it. Today, we will discuss the essential roles from conception to completion.   1. Domain Expert & Product Owner - Navigators   AI/ML projects start with SMEs and business experts. They identify gaps and opportunities and link company strategy with data-centric goals. Their subject expertise and business insights guide the AI/ML journey.   2. Data Engineer - Data Alchemists   Data preparation follows business needs. Data engineers design the systems that store, manage, and gather all this data. They format, wrangle, and pre-process data to prepare it for analysis.   3. Data Scientists - Insight Miners   AI/ML data scientists investigate and discover. They use statistical and ML algorithms to find patterns, insights, and prediction models in pre-processed data. They continually modify their models to transform raw data into value.   4. Machine Learning Architects - Blueprint Designers   AI/ML ecosystem strategists are ML architects. They choose methods, features, and data sets for ML solutions. They collaborate with ML engineers and data scientists to guarantee model translation into production, scalability, reliability, and performance.   5. Machine Learning Engineers: Bridge-Builders   Data science meets software engineering in ML engineers. They design, optimize, and turn ML models into robust, scalable, and efficient software solutions. They develop systems to handle real-time data, integrate the model into operations, and solve latency concerns.   6. DevOps Engineers and Technical Architects - Strategists & Implementers   Finally, DevOps engineers and technical architects implement AI/ML models. Deployment, monitoring, security, and scalability depend on them. They employ innovative technologies to track model performance and change depending on real-world feedback to ensure long-term success and business alignment.   The AI/ML lifecycle is a well-orchestrated symphony of roles. Each player, from SMEs establishing the direction to DevOps engineers implementing and monitoring the models, is crucial. This process goes well with my favorite quote "All of us are smarter than one of us"   Understanding this dynamics, make AI/ML projects effective and impactful. How does your company manage the AI/ML lifecycle? Are there any additional responsibilities that you consider essential to this journey?   Reference: Deloitte, Nasscom, McKinsey, PwC

Think you know what it takes to land an IT Project Manager role in 2025? Think again. I analyzed 164 real job postings across the U.S. and Canada. The results paint a clear—and unexpected—picture of what hiring managers actually want in 2025. If you're aiming to land a more strategic or better-paying IT PM role, this might change your approach. Here's what the market is really asking for 👇 ▶️ The Role Looks the Same on Paper, But It's Not IT Project Manager can mean: • Leading cybersecurity transformations • Migrating cloud platforms without breaking production • Managing AI/ML systems nobody understands yet • Running ERP upgrades that touch every department ✔️ If your resume sounds generic, you're invisible. You need to show domain expertise and business impact. ▶️ Where the Real Opportunities Are Forget "tech companies." Everyone's hiring IT PMs now: • Healthcare (digital transformation) • Finance (legacy modernization) • Government (security overhauls) • Consulting (client rescues) ✔️ They don't want template followers. They want PMs who can navigate chaos without a playbook. ▶️ The Remote Dream Is Dying • 53% hybrid • 27% on-site • 20% remote ✔️Translation: If you're banking on remote-only, you're fishing in a shrinking pond. ▶️ Salary Benchmarks (U.S.-Weighted) • Typical range: $106K–$159K • Senior/specialized roles: $160K–$299K • Canadian salaries: Align proportionally with mid-to-senior U.S. roles ▶️ What Skills Are in Demand? ✔️ Hard Skills: • Agile (49%) – table stakes • Cybersecurity (42%) – the new gold • Infrastructure (30%) – servers, networks, IT systems • Cloud: Azure (13%), AWS (9%) – adoption still growing • Tools: Jira, Confluence, DevOps, SDLC, ERP (SAP) • AI/ML projects: 16% – trend to watch • DevOps & CI/CD – often mentioned alongside Agile • ERP expertise – strong in enterprise transformation roles 🧠 Tech literacy is no longer optional—it's a positioning advantage. Knowing how these systems drive value is what separates the top 10%. ✔️ Certifications: • PMP (51%) • Scrum Master (11%) • Others: ITIL, AWS, CISSP—rarely required, but can complement your story ✔️ Soft Skills • Communication (77%) • Leadership (72%) • Stakeholder management (33%) • Collaboration, adaptability, strategic thinking ▶️ Strategic Takeaways 1- Generic PMs are getting filtered out. If your resume reads like a task list, you're losing interviews to PMs who speak in outcomes and impact. 2- Certifications alone don't differentiate you. The PMP signals credibility. But what closes offers is context, how you applied what you know to drive real change. 3- IT PMs must specialize. Same title, wildly different expectations. You must position yourself by domain—cloud, infra, cybersecurity, SaaS, ERP—not just by title. 📌 Ready to position yourself for a high-impact IT PM role in 2025? → Go to the top comment and apply to work with me 1:1. → Repost ♺ to help others in your network. Follow Jesus Romero for weekly PM career strategies.

A decade ago, the boundary between Product Management and Engineering was very clear. Product managers focused on requirements, roadmaps, customer conversations, and prioritization. Engineers focused on system design, architecture, and building software. There was some overlap, but it was thin and deliberate. That separation made sense at the time. In today’s AI-driven world, that boundary is fading fast. With modern AI tools and vibe coding workflows, getting a working POC no longer requires weeks of detailed handoffs. Ideas can move from concept to something tangible in days, sometimes hours. In the past, a typical flow looked like this. A product manager wrote a PRD. Engineers interpreted it. The first real output appeared after multiple sprints. Feedback loops were slow and expensive. Today, the workflow is very different. Using AI-assisted coding, agents, and scaffolding tools, I can explore ideas end to end. I can think through the customer journey, define feature behavior, prototype logic, and validate feasibility early. Many assumptions get tested before formal engineering cycles even begin. This is completely changing the nature of the role. Product managers are no longer limited to conceptual ownership. They are increasingly shaping solutions at a technical level. Engineers, in parallel, are deeply involved in product decisions from day one. This is how Product and Engineering roles are blending into a Product and Engineering role. From my own experience, the technical depth I can reach today in AI product work is far deeper than before. I still need to understand product vision, customer journeys, and core product management fundamentals. But I also need to engage with architecture, model behavior, orchestration patterns, and system-level tradeoffs. AI tools make this possible. They compress learning curves and shorten feedback loops, but they also raise expectations. Staying shallow is no longer an option. Looking ahead, I see the intersection of Product and Engineering growing significantly. Over time, we may end up with thinner layers of dedicated Product roles and dedicated Engineering roles, with a much larger core where both blend together. I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar

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?

🚨 After months of analyzing interview experiences, job descriptions, hiring trends, and discussions with professionals across IT Audit, SOX, GRC, IAM, PAM, Risk Management, and Compliance… I noticed something surprising. The questions change. The companies change. The tools change. But the core interview questions remain almost the same. Every candidate spends hours searching for: ❓ What will they ask? ❓ What should I prepare? ❓ Which topics are most important? ❓ How deep should my understanding be? The truth is: Most interviewers are not trying to confuse you. They’re trying to understand whether you can think like an auditor, a risk professional, or a compliance practitioner. That’s why the same themes keep appearing: 🔹 SOX Audit 🔹 ITGC 🔹 ITAC 🔹 IAM 🔹 PAM 🔹 Access Reviews 🔹 Risk Assessment 🔹 Compliance Frameworks 🔹 GRC If you can confidently answer questions from these domains, you’re already covering a significant portion of what is typically discussed in IT Audit, Cybersecurity Governance, Risk Management, and Compliance interviews. And here’s something most candidates overlook: Interviewers are often less interested in textbook definitions and more interested in: ✅ How you performed testing ✅ What evidence you collected ✅ What risk you identified ✅ What observations you raised ✅ What recommendations you provided That’s what separates a candidate who has studied the topic from someone who has actually worked on it. I’ve compiled some of the most repetitive questions I’ve seen across these domains in the infographic. 📌 Save this post. 📌 Use it as your interview checklist. 📌 Revisit it before every interview. And because many of you asked for it… 🔥 In my next posts, I’ll be sharing practical, interview-ready answers to these questions one domain at a time. Starting with: SOX Audit → ITGC → ITAC → IAM → PAM → Risk Management → GRC 💬 Which topic would you like me to cover first? 1️⃣ SOX Audit 2️⃣ ITGC 3️⃣ ITAC 4️⃣ IAM 5️⃣ PAM 6️⃣ Risk Management 7️⃣ GRC Comment the number below 👇 And if this helped, follow for more practical content on IT Audit, Cybersecurity, GRC, Risk Management, Compliance, SOX, IAM, PAM, and Audit Interviews. #ITAudit #GRC #SOX #ITGC #ITAC #CyberSecurity #RiskManagement #Compliance #IAM #PAM #AccessReview #InternalAudit #TechnologyRisk #AuditCareer #CyberRisk #GovernanceRiskCompliance #InterviewPreparation #CareerGrowth #InformationSecurity #RiskAssessment #AuditTips #ISACA #CISA #ISO27001 #NIST #SOC2 #TechnologyAudit #JobSearch #Upskilling #ProfessionalDevelopment

I wish someone had shown me this pyramid on Day 1 of my IT audit career. Would've saved me 6 months of confusion. When I started, I jumped straight to controls. Access reviews. Change management. Backup testing. I was checking boxes. But I had no idea WHY those controls mattered. No one told me to start at the top of the pyramid. The Business. What does this company actually do? How do they make money? What goals are they chasing? Without understanding that, every control I tested felt random. Then one day, my manager asked me: "Chinmay, why this IT Application is in scope for our audit?" I froze. Because I was testing controls in isolation. I never connected controls to IT apps and IT apps to the business process. Great auditors don't start at the bottom of the pyramid. They start at the top. You can't test what you don't understand. This framework changed everything for me. Understand the business → What goals drive this company? Map the core processes → What processes support those goals? Identify the applications → What systems enable those processes? Evaluate IT risks → What can go wrong in those systems? Test the controls → What mitigates those risks? Top to bottom. Always. If you're confused about where to start, save this infographic. Print it. Keep it at your desk. Because the biggest mistake I made wasn't bad testing. It was testing without context. Learn IT audit the way it's actually done. Because clarity is the difference between doing audit and understanding it. Tag someone who needs to see this framework. #itaudit #audit #risk #compliance #internalaudit #cisa #isaca

𝐍𝐨𝐭 𝐥𝐨𝐧𝐠 𝐚𝐠𝐨, 𝐩𝐞𝐨𝐩𝐥𝐞 𝐮𝐬𝐞𝐝 𝐭𝐨 𝐬𝐚𝐲 — “Soon, Data Analysts and Data Scientists will merge into one.” Now fast forward 2 years... It’s not just Analysts and Scientists — even Data Engineering roles are blending into the same pipeline. Today, whether you're applying as a Data Analyst, Scientist, or Engineer — companies are expecting you to: ✅ Know SQL & Python ✅ Understand how data flows end-to-end ✅ Build and validate basic ETL pipelines ✅ Clean & transform data for analysis ✅ And even contribute to predictive models (for churn, demand, retention) This overlap is not a theory anymore — it's exactly what I’ve seen in multiple interviews and projects across roles. In fact, for many mid-level roles — the first half of the job is about bringing the right data to the table (engineering mindset)… The next part is extracting insights and building reports (analytics)… And if the project demands, the final layer could be running a regression or time series forecast (basic data science). So yeah, the lines are getting blurrier than ever — and the smart move is not to panic… But to build a versatile skill stack that lets you contribute across this full journey. And trust me — it’s possible. You don’t need 20 tools. Just strong SQL, Python, Data Sense, and curiosity to build end-to-end.