Key Focus Areas for Platform Engineers

💡 "AI might grab the spotlight… but it’s the data and platform engineers who plug in the lights." I see everyone's talking about AI like it just happens. Spoiler: it doesn't. Behind every smooth prediction and clever recommendation are two groups of people making sure things actually work. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: Your AI's Reality Check AI models need data. Good data. Not just any data dumped into a folder somewhere. What they're actually doing: → Building pipelines that pull data from ten different sources and make them play nice together → Cleaning up the mess so your model isn't learning from typos and duplicates → Setting up quality checks because one corrupted field can tank your entire output → Creating systems for data storage, transformation, and access that don't fall apart at scale They're not building the AI. They're building the foundation it stands on. Without them, your fancy model is just expensive guesswork running on bad information. 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: The Reason It Doesn't Crash You trained a model. Great. Now how do you actually deploy it without everything catching fire? Enter platform engineers: → Building infrastructure that handles real traffic, not just your test dataset → Managing deployments so updates don't break production at 2am → Monitoring systems to catch issues before your users do → Scaling resources so your app doesn't choke when it actually gets popular → Dealing with security, networking, and compliance (the stuff nobody wants to think about) They make sure your #AI goes from "works on my laptop" to "works for a million people." So, while the Data Scientist is teaching the model how to shine, the Data Engineer is wiring up the lights so they actually turn on, and the Platform Engineer is powering the grid to keep them glowing steadily. Data Engineers holding everything together again 💪🙂 Agree?

Before you hit ‘Apply’ on that DevOps job — make sure you know which role it really is. You’re learning Docker, Kubernetes, Terraform, Git… But here’s the twist: Those tools show up in every job title — 𝐃𝐞𝐯𝐎𝐩𝐬, 𝐒𝐑𝐄, 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫, 𝐂𝐥𝐨𝐮𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫, and more. And if you don’t understand the 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞, you could be preparing for the wrong thing. So let’s break it down — 𝐫𝐞𝐚𝐥𝐢𝐬𝐭𝐢𝐜𝐚𝐥𝐥𝐲, not just definitions from Google. 𝐃𝐞𝐯𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 The generalist role — someone who glues everything together. • CI/CD pipelines (Jenkins/GitHub Actions) • Infrastructure as Code (Terraform) • Docker + Kubernetes for deployments • Monitoring setup and basic alerts 𝐆𝐨𝐚𝐥: Ship software faster, safer, and repeatably.' 𝐂𝐥𝐨𝐮𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 Knows the cloud like the back of their hand — AWS, GCP, Azure. • VPCs, IAM, EC2, S3, Load Balancers • Network setup, security groups, VPNs • Cloud-native deployments & serverless setups 𝐆𝐨𝐚𝐥: Design and maintain scalable cloud infrastructure. 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 They build the internal tools dev teams use. • Create self-service deployment platforms • Manage Terraform modules for teams • Build reusable pipelines and secrets management • Support devs with automation & platform APIs 𝐆𝐨𝐚𝐥: Make developers faster and infra consistent. 𝐒𝐢𝐭𝐞 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 (𝐒𝐑𝐄) Lives and breathes reliability. • Incident response and postmortems • Uptime, latency, and performance metrics • Monitoring (Prometheus/Grafana), alerting • SLOs, SLIs, error budgets 𝐆𝐨𝐚𝐥: Keep production alive — even when it’s on fire. 𝐃𝐞𝐯𝐒𝐞𝐜𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 Brings security into DevOps workflows. • Integrates security scans into pipelines • Manages secrets (Vault, AWS Secrets Manager) • Helps with compliance and secure deployments 𝐆𝐨𝐚𝐥: Shift security left and bake it into CI/CD. 𝐑𝐞𝐥𝐞𝐚𝐬𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 Manages everything from versioning to rollout. • Release pipelines and canary deployments • Version control, tagging, rollback • Works closely with dev + QA + ops 𝐆𝐨𝐚𝐥: Deliver updates without breaking production. 𝐒𝐨, 𝐰𝐡𝐢𝐜𝐡 𝐨𝐧𝐞’𝐬 𝐟𝐨𝐫 𝐲𝐨𝐮? • Starting out? → Go for 𝐃𝐞𝐯𝐎𝐩𝐬/𝐂𝐥𝐨𝐮𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 • Love fixing things and uptime? → Explore 𝐒𝐑𝐄 • Like building platforms for others? → Dive into 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 • Care about security and compliance? → Try 𝐃𝐞𝐯𝐒𝐞𝐜𝐎𝐩𝐬 Same tools. Different goals. Choose wisely — because clicking "Apply" is just the start. Drop your target role below — I’ll share resources to help you prep!

Does AI Kill Platform Engineering? AI is disrupting almost every layer of software. Code, testing, security, support, product management. It is reshaping how systems are built and operated. So it is fair to ask what it means for platform engineering. Two questions keep coming up in conversations with enterprise leaders, platform teams and investors: 1. If AI can operate infrastructure, why do we need platform engineering at all? 2. As AI infrastructure becomes dominant, do cloud-era platforms still matter? Let’s start with the first. The original case for platform engineering was productivity. Self-service. Golden paths. Reducing cognitive load. But if AI becomes the interface, that argument weakens. So what’s left? Control. Enterprises do not optimize purely for capability. They optimize for accountability. Someone still owns the cloud bill, the compliance audit, data residency, security posture, and the blast radius of failure. An AI agent can provision infrastructure. It cannot assume responsibility. As AI increases velocity, governance becomes more important, not less.And this is where declarative (intent based) APIs matter. Agents need structured, stable, idempotent interfaces. They need to declare intent, not execute fragile imperative steps. They need policy enforcement and reconciliation built in. Platform engineering becomes less about productivity tooling for humans and more about defining the declarative control plane that agents operate against. Now the second question. AI workloads introduce GPUs, accelerators, model registries, inference endpoints. But underneath, it is still compute, networking, storage, identity, policy, and cost. The workload changes. The hardware shifts. The need for a governed substrate does not. If anything, AI increases heterogeneity, cost volatility, and regulatory scrutiny. What I’m seeing in Fortune 500 companies: Platform teams are not shrinking. They are being asked to support traditional workloads plus AI infrastructure, across more clouds, at higher velocity, under stricter compliance. The scope is expanding. The real debate isn’t whether AI kills platform engineering. It’s whether enterprises still want sovereignty and policy control over infrastructure in an AI-driven world. From what I’m seeing, they clearly do. Curious what others are experiencing. Is AI shrinking your platform scope, or redefining it? #PlatformEngineering #AIInfrastructure #CloudNative #Crossplane #EnterpriseIT

𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 Modern platforms must be secure, resilient, and globally scalable. After years of working with architects, engineers, and product leaders, one thing has become clear: most system failures are not caused by bad code but by poor design choices. The System Design Topic Map consolidates the twelve foundational pillars you must master to architect reliable, enterprise-ready systems: 𝟭. 𝗧𝗿𝗮𝗳𝗳𝗶𝗰 𝗮𝗻𝗱 𝗘𝗱𝗴𝗲 Design entry points with load balancing, CDN caching, adaptive throttling, and WAF integration for security and performance. 𝟮. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗮𝗻𝗱 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Enable reliable connectivity with HTTP, WebSockets, gRPC, and service discovery strategies that keep distributed systems synchronized. 𝟯. 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿 Design storage that fits the workload: SQL for structure, NoSQL for flexibility, and distributed models with sharding and replication for scale. 𝟰. 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 Deliver sub-second responses with multi-tier caching, eviction strategies, and latency reduction techniques like hedged requests. 𝟱. 𝗠𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 Decouple services with Kafka, RabbitMQ, SQS, or EventBridge. Enable event-driven pipelines and exactly-once delivery for fault tolerance. 𝟲. 𝗦𝗲𝗮𝗿𝗰𝗵 𝗮𝗻𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 Power semantic search, hybrid ranking, and analytics at scale using indexing strategies and vector-enhanced queries. 𝟳. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 𝗮𝗻𝗱 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 Run workloads efficiently with Kubernetes, containers, serverless compute, and autoscaling across environments. 𝟴. 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Plan for failure with circuit breakers, graceful degradation, cross-region failover, and chaos testing frameworks. 𝟵. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 Protect systems using IAM, OAuth2, encryption, and secure defaults that enforce the principle of least privilege. 𝟭𝟬. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 Monitor health with metrics, traces, logs, dashboards, and SLO-driven alerting for proactive detection. 𝟭𝟭. 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗮𝗻𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Accelerate releases with CI and CD pipelines, infrastructure as code, rolling updates, and feature flag-driven rollouts. 𝟭𝟮. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁, 𝗖𝗼𝘀𝘁, 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 Integrate FinOps, GDPR, HIPAA, and SOC2 strategies to optimize cost, enforce policies, and scale responsibly. The System Design Topic Map is your blueprint to build platforms that are resilient, intelligent, and trusted by millions. Follow Umair Ahmad for more insights #SystemDesign #Architecture #CloudComputing #DevOps #EngineeringLeadership

Too many people think platform engineering is about gluing tools together with YAML. It’s not. The job is to make it easy and safe for teams to use infrastructure the way they need to, without opening a ticket or having everyone become a security or compliance expert. Good platforms abstract complexity without removing responsibility. They provide guardrails so developers can move fast and stay safe. With AI-assisted development on the rise, this becomes even more important. Platform engineers need to build validation agents that automatically apply organizational safeguards, along with examples and training on how to use them, so that every team can leverage that knowledge without having to learn on their own. Platform engineering is hard when it's done correctly. You must stay a step ahead of your users without building things they’ll never use. That means deep understanding of the problems they face daily, solid product management skills, and constant engagement with the teams you serve. It’s not about YAML. It’s about engineering a developer experience that accelerates good outcomes.

🔍 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐂𝐡𝐞𝐜𝐤: 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐏𝐮𝐥𝐬𝐞 𝐑𝐞𝐩𝐨𝐫𝐭 𝐑𝐞𝐯𝐞𝐚𝐥𝐬 The moment a report asks “what organizations are actually doing?”, you know it’s valuable. Octopus Deploy’s Platform Engineering Pulse reveals how companies implement Platform Engineering today — what’s working, what isn’t, and why. Great report published by Octopus Deploy : https://lnkd.in/eCYg3aev Here are four observations worth your attention: 1. Theory vs. Reality → A gap still exists While Platform Engineering is widely discussed in theory, the report finds many teams struggle with the execution side. The features that get built don’t always align with the business value intended. In fact, without clear goals and measurement, you risk slipping into the “success illusion.” 2. Measurement matters more than ever One of the most actionable findings: organizations that measure multiple dimensions of platform performance are significantly more likely to succeed. Tracking just one metric? You’re rolling the dice. The recommended MONK-metrics framework (Market share, Onboarding time, NPS, Key customer metrics) offers a balanced path. 3. Features that really drive value The report highlights that top platform teams focus on build automation, deployment automation, infrastructure automation, test automation, and monitoring/observability. Miss any of these? You’re missing a foundational piece. 4. Adoption strategy is about product-thinking, not mandates Perhaps counterintuitively, platforms that choose to be used vs . those that are mandated show stronger developer satisfaction and engagement. If your platform requires too much forcing, you’re fighting human behavior instead of enabling it. 💡 Why this should matter to you as a leader, builder or platform champion If you’re just starting your platform journey, use this report as a roadmap: define goals → select key features with business alignment → measure thoughtfully → evolve. ⁉️ If you’re looking to optimize an existing initiative, ask: are you measuring multiple dimensions? Are developers choosing your platform? Are your features aligned with developer experience not just infrastructure? If you’re leading change across DevOps, Engineering or Platform teams, you now have data-backed insights to speak language leadership understands: productivity gains, developer experience, cost and risk. ✨ Platform Engineering isn’t a checkbox — it’s a strategic lever. The Pulse report from Octopus invites us to move beyond the hype and ask harder questions: Are we solving the right problem? Are we measuring value? Are we building something people actually want to use? For anyone building platforms, this report isn’t just reading — it’s needed. AsembleAI TLDR #PlatformEngineering #InternalDeveloperPlatform #DevOps #DeveloperExperience #EngineeringLeadership #SoftwareDelivery #Innovation #MetricsMatter #OctopusDeploy #AsembleAI

In recent years, the conversation around platform engineering has shifted from developer experience to the foundation that makes it possible - Infrastructure. Seeing Facets.cloud included in Gartner’s Reference Architecture Brief: Infrastructure Platform Engineering underscores the relevance of the work our teams have been doing to redefine that foundation. Our philosophy has been simple - treat infrastructure as a product. That means predictable environments, governed automation, and self-service built into the core. Infrastructure platform engineering is not a new category; it is the natural outcome of every organization trying to reconcile speed with safety, flexibility with consistency, and autonomy with accountability. At Facets, we are building the orchestration layer that makes this possible. A unified control plane where teams can provision, deploy, and operate environments through reusable blueprints and policies that enforce compliance by design. When done right, infrastructure stops being a dependency and becomes a multiplier that accelerates delivery instead of slowing it. Appreciate the mention from Lucas Albuquerque and Gartner. As more enterprises move toward unified infrastructure platforms, this acknowledgment helps accelerate much-needed clarity in how teams approach scale and automation. Happy to share how this approach is making it easier for teams to build and operate their platforms. Anshul Sao Rohit Raveendran Gaurav Mehta

For platform engineers, the AI wave means two things: you need to build with AI and build for AI. And with 75% of platform engineers already running AI workloads or gearing up to, adoption is moving fast, and the learning curve is steep. This paper is both: -a report based on a survey of 300+ platform engineers: what they’re doing, what’s working, and how AI is reshaping the role AND -a practical playbook with best practices for using AI to build platforms and how platforms should evolve to host AI workloads. It's an initiative from the Platform Engineering community, written by an amazing group: Sam Barlien Luca Galante, Ajay Chankramath, Rickey Zachary, and me.😬👋 Big thanks to Kaspar Von Grünberg for bringing me in; I had a blast contributing.