OutcoreX’s Post

You do not need “talent”. You need output. A lot of hiring language is complete nonsense. “We need top talent.” “We want rockstars.” “We are looking for the best.” Fine. And what exactly is blocked? Backend APIs? AI integration? Data pipelines? Java services? Platform scaling? Most companies are terrible at this. They speak in slogans instead of operational needs. Good engineering support starts with reality: what is broken, what is late, what needs to be built, and how fast someone can contribute. That is how we think at OutcoreX. Python, AI, and Java engineers focused on execution, not buzzwords. #python #java #ai #softwareengineering #delivery #hiring

Most engineering resumes I see talk about tech stack and responsibilities. 𝗩𝗲𝗿𝘆 𝗳𝗲𝘄 𝘁𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁 𝗶𝗺𝗽𝗮𝗰𝘁. In 15 years of hiring engineers & product (including AI teams), one thing is clear: numbers get attention. Hiring managers don’t read line by line, they scan for measurable outcomes. For engineers, that means -  • 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗔𝗣𝗜 𝗹𝗮𝘁𝗲𝗻𝗰𝘆 𝗳𝗿𝗼𝗺 𝟯𝟬𝟬𝗺𝘀 → 𝟭𝟮𝟬𝗺𝘀  • 𝗛𝗮𝗻𝗱𝗹𝗲𝗱 𝘀𝗰𝗮𝗹𝗲 𝗳𝗿𝗼𝗺 𝟭𝟬𝗞 → 𝟭𝗠 𝘂𝘀𝗲𝗿𝘀/𝗺𝗼𝗻𝘁𝗵  • 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺 𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁 𝗯𝘆 𝟮.𝟱𝘅  • 𝗖𝘂𝘁 𝗰𝗹𝗼𝘂𝗱 𝗰𝗼𝘀𝘁 𝗯𝘆 𝟮𝟴%”  • 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗺𝗼𝗱𝗲𝗹 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗳𝗿𝗼𝗺 𝟴𝟮% → 𝟵𝟭%  • 𝗗𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝘂𝘀𝗲𝗱 𝗯𝘆 𝟱𝗟+ 𝘂𝘀𝗲𝗿𝘀 This is what makes someone stop and shortlist. Your stack (Java, Python, React) is expected. 𝗬𝗼𝘂𝗿 𝗶𝗺𝗽𝗮𝗰𝘁 𝗶𝘀 𝗻𝗼𝘁. In high-quality engineering & AI hiring, if your resume doesn’t show numbers, it gets skipped. Stop writing what you worked on. Start writing what changed because of you. 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝗶𝗻 𝗮 𝟲-𝘀𝗲𝗰𝗼𝗻𝗱 𝘀𝗰𝗮𝗻, 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗱𝗲𝗰𝗶𝗱𝗲 𝘆𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆. #EngineeringCareers #AIHiring #TechHiring #ResumeTips #ProductEngineering #StartupHiring Harish Joshi Malvika Gautam Vinay Tiwari Madhavi Sengar Rishabh Aggarwal

Your hiring process isn’t filtering talent. It’s filtering out the best ones. Most companies are still running 30 to 60 day hiring cycles for AI engineers and Python developers. It sounds thorough. But here is what actually happens: You are scheduling another round They are accepting another offer You are aligning internally They are already onboarding Top candidates do not wait. They move in 48 to 96 hours. So if your process takes weeks, you are not competing for the top 1 percent. You are competing for whoever is left. And then comes the conclusion “Good talent is hard to find.” Not really. Good talent is hard to hold when your process is slow. The best teams do not rush decisions. They remove friction. Clear role definition Fewer and sharper interviews Faster decision making 4 days is becoming the benchmark. Anything beyond that means you are out of sync with the market. Curious to hear from others How long does it take you to close a strong candidate? https://lnkd.in/gQaN324p #Hiring #TechHiring #AIEngineers #PythonDevelopers #StartupHiring #TalentAcquisition #Founders #Recruitment #HiringStrategy #ScalingTeams #Leadership #BuildingBlocks

Python developers continue to be some of the most versatile and in-demand professionals in today's tech landscape, powering everything from data pipelines to AI applications. But hiring the right Python talent goes beyond coding ability. It is about finding developers who can solve complex problems, adapt across use cases, and deliver real business impact. In a competitive market, the best candidates are not actively applying. They are already contributing elsewhere and need to be engaged through trusted relationships. At KORE1, we connect you with Python developers we know and trust, helping you hire faster and with confidence. If you are building data-driven or scalable solutions, the right hiring strategy makes all the difference. Learn more: https://lnkd.in/gW5EZNMg #KORE1 #Python #Hiring #TechTalent

Hiring AI Engineers? Stop filtering for perfection...it doesn't exist. Most AI engineers shipping real product aren’t “pure ML purists”. Hot take: The best hires right now are hybrid builders. Look for: Strong Python + systems thinking Some LLM / RAG exposure Product intuition (this is huge) If they’ve shipped even 1 production AI feature, pay attention. Want help pressure-testing your shortlist? Or maybe adding a few more candidates to it? Drop me a message or grab time here: https://lnkd.in/dFX9B5rB

In the Python community across the Netherlands, the world is smaller than most people think. The developer you don’t hire today might be the hiring manager you’re speaking with in two years. I’ve seen it happen repeatedly. A Python developer goes through multiple interview stages. They invest time preparing. They review your architecture. They start to picture themselves in the team. Then timelines slip. Feedback takes weeks. Communication becomes vague instead of clear. Even when the final answer is no, how that moment is handled matters. Developers remember: - Whether the role was clearly defined (backend vs data vs AI) - How transparent the process was - Whether feedback was constructive - How they were treated when they weren’t selected Python careers move fast. Backend developers move into platform roles. Data engineers become ML leads. Senior developers become CTOs at startups. You don’t need to hire everyone you meet. But you should treat every developer as someone you may work with again. Because in this market… you probably will!

𝗥𝗼𝗹𝗲 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗱 𝗦𝗲𝗿𝗶𝗲𝘀 | 𝟬𝟮 👩💻 𝗝𝗮𝘃𝗮 𝘃𝘀 𝗣𝘆𝘁𝗵𝗼𝗻: If you’re hiring for it, you should know when to pick what. Because “any developer chalega” is exactly how bad hires happen. 𝗟𝗲𝘁’𝘀 𝗺𝗮𝗸𝗲 𝗶𝘁 𝗰𝗹𝗲𝗮𝗿.... 𝗝𝗮𝘃𝗮 = Built for performance, stability, and large-scale systems Used where runtime efficiency, security, and system reliability matter. Think: banking systems, enterprise apps, large backend architectures. 𝗣𝘆𝘁𝗵𝗼𝗻 = Built for flexibility, simplicity, and faster development Used where quick iterations, experimentation, and data handling matter. Think: AI/ML, data science, automation, startups, prototypes. 𝗦𝗶𝗺𝗽𝗹𝗲 𝘄𝗮𝘆 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱: If your requirement is: High-performance, large-scale backend systems → Go for Java Faster development, experimentation, and data-heavy work → Go for Python 𝗥𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱 𝗵𝗶𝗿𝗶𝗻𝗴 𝗰𝗼𝗻𝘁𝗲𝘅𝘁: Fintech → Java (performance + reliability) Startups → Python (faster MVPs) Data/AI roles → Python dominates Enterprise SaaS → Often Java (or both) 𝗪𝗵𝗲𝗿𝗲 𝗿𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀 𝗴𝗼 𝘄𝗿𝗼𝗻𝗴: Hiring Python devs for performance-critical backend systems Hiring Java devs for roles needing rapid experimentation 𝗔𝗻𝗱 𝗻𝗼𝘄, 𝗔𝗜 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝘁𝗵𝗲 𝗴𝗮𝗺𝗲: Python → Leading in AI/ML ecosystem Java → Strong in scalable, production-grade systems 𝗦𝗼 𝗶𝘁’𝘀 𝗻𝗼𝘁 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗶𝗰𝗵 𝗶𝘀 “𝗯𝗲𝘁𝘁𝗲𝗿” 𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗳𝗶𝘁 𝗳𝗼𝗿 𝗽𝘂𝗿𝗽𝗼𝘀𝗲. If we don’t understand this nuance, we don’t just slow teams down, we hire wrong. Day 2 of #RoleSimplifiedSeries #Recruitment #Hiring #TechHiring #TalentAcquisition #RecruiterLife #Java #Python #TechRoles #HRCommunity #LinkedInIndia #CareerGrowth #HiringTrends

I talk to a lot of CTOs mentioning that they want to take advantage of the "employers market" that we're in, though, that's not always the way to look at it depending on the positions... Junior hiring is down 25% at Big Tech, with focus shifting to senior engineers who can validate AI outputs. Engineering leaders are increasingly asking for senior+ only - which compresses the talent pool and drives up comp expectations. Here is what we're seeing the most demand for: - AI / ML Engineers (Python, PyTorch, TensorFlow, RAG pipelines, and LLM fine-tuning) - NLP / LLM Engineer (people who know language models, prompt engineering, fine-tuning, and RAG pipelines) - Backend Engineers (Full-stack and backend engineers that are AI-adjacent) - DevOps / Platform / SRE (still very in demand as the complexity on the backend has increased with AI) - Data Engineers (consistently needed across every vertical in NYC) - Cybersecurity Engineers (up 125% YoY) - AI Product Managers (people who own AI-native products end-to-end, hands-on experience with LLMs, agentic coding tools, or AI prototyping platforms - not just familiarity. Also fluency in system design (APIs, microservices, etc.) 

Had an interesting interview today. Candidate: 5 years experience in Java. We started the discussion and things sounded quite strong initially. He spoke about microservices, some production issues he had handled, even mentioned race conditions and concurrency. At that point, it felt like a solid profile. Then I moved to a few basic questions: – OOPS concepts – how static actually works – difference between types of variables And that’s where things changed. The answers were either unclear or incomplete. Not something you’d expect from someone with 5 years of experience. It got me thinking… Are we focusing too much on high-level concepts and skipping the fundamentals? Or is it becoming acceptable because frameworks, tools, and now AI are doing most of the heavy lifting? Personally, I don’t think fundamentals can be ignored. You can talk about architecture all day, but when something breaks in production, it usually comes down to basics. If those aren’t clear, debugging becomes guesswork. AI can definitely help us write code faster. But without understanding, how do we know if the code is even correct? Still deciding what I would do in this case. Would you hire someone like this? Or do you see this as a red flag? #Java #Hiring #SoftwareEngineering #Interviews #CareerGrowth #TechCareers #Hiring #TechInterviews #InterviewExperience #Recruitment #HiringDevelopers #CareerGrowth #JobSearch #DeveloperJobs #AI

🚨 Java Developers. Are you still avoiding AI because it’s “Python-heavy”? Here’s something worth your attention 👇 👉 Deep Java Library (DJL) bringing AI/ML capabilities directly into the Java ecosystem. As a Java Full Stack Developer, I’ve been exploring how we can integrate AI into production-grade backend systems without switching stacks. 💡 What makes DJL interesting: ✔️ Build & run Deep Learning models natively in Java ✔️ Seamless integration with TensorFlow, PyTorch, ONNX ✔️ Use pre-trained models or plug in custom ML pipelines ✔️ Deploy easily on AWS, Azure, or on-prem systems 🔧 Why this matters for backend engineers: → No need to depend entirely on Python-based services → Easier integration with existing Java microservices → Faster adoption of AI in enterprise systems → Cleaner architecture for real-time intelligent applications 📌 Where I see real use cases: Fraud detection systems Recommendation engines Intelligent document processing Real-time analytics with event-driven systems ⚡ As someone working on scalable microservices & cloud-native systems, this opens up a new layer of capabilities within Java itself. If you're a recruiter or hiring manager looking for engineers who can bridge Backend + AI, this is the kind of direction I’m actively exploring. Happy to connect or discuss opportunities 🤝 #Java #SpringBoot #MachineLearning #DeepLearning #DJL #BackendDevelopment #Microservices #AI #AWS #Hiring #OpenToWork #C2C #seniordeveloper #javadeveloper