"Speculative Decoding Boosts LinkedIn's Hiring Assistant"

Prompt Engineers make $136K average by crafting the right words. They don't write code. They write instructions that make AI systems work better. The AI job market is exploding. And you don't need coding skills to get in. Three no-code AI roles are paying serious money: ✅ AI Strategist: $130K-$209K (Google pays up to $267K) ✅ Prompt Engineer: $136K average ✅ AI Content Reviewer: $44K-$84K What makes these roles different? They focus on business strategy. Creative problem-solving. Critical thinking. Not coding. The skills that matter: • Business analytics • Data literacy • Natural language processing knowledge • Creative writing • Strategic thinking Companies need people who understand AI tools. People who can optimize prompts. People who can align AI with business goals. The barrier to entry? Lower than you think. Certifications from MIT Sloan, Google, and HubSpot can get you started. Eight out of ten executives now back AI agents. Operations teams are leading adoption. This isn't just a tech trend. It's a career shift. The message is clear: Strong analytical skills beat coding skills in today's AI landscape. What's stopping you from exploring these opportunities? #AI #NoCode #CareerGrowth 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/e5fCxnj5

Prompt Engineers make $136K average by crafting the right words. They don't write code. They write instructions that make AI systems work better. The AI job market is exploding. And you don't need coding skills to get in. Three no-code AI roles are paying serious money: ✅ AI Strategist: $130K-$209K (Google pays up to $267K) ✅ Prompt Engineer: $136K average ✅ AI Content Reviewer: $44K-$84K What makes these roles different? They focus on business strategy. Creative problem-solving. Critical thinking. Not coding. The skills that matter: • Business analytics • Data literacy • Natural language processing knowledge • Creative writing • Strategic thinking Companies need people who understand AI tools. People who can optimize prompts. People who can align AI with business goals. The barrier to entry? Lower than you think. Certifications from MIT Sloan, Google, and HubSpot can get you started. Eight out of ten executives now back AI agents. Operations teams are leading adoption. This isn't just a tech trend. It's a career shift. The message is clear: Strong analytical skills beat coding skills in today's AI landscape. What's stopping you from exploring these opportunities? #AI #NoCode #CareerGrowth 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/dBGkpMkB

🚀 First Milestone Achieved: Building a Smarter AI Recruiter After weeks of experimentation and fine-tuning, I’m excited to share the first completed part of my AI Recruitment Assistant project an intelligent system that matches candidates to job descriptions using semantic understanding and graph-based reasoning 🧠 🧩 What We’ve Built So Far ✅ Data Engineering & Preprocessing Cleaned and unified two major resume datasets (DS1 & DS2). Standardized 24 job categories (ENGINEERING, IT, FINANCE, etc.). Extracted technical skills while preserving key tokens (C++, .NET, TensorFlow, etc.). ✅ Fine-Tuned Transformer Model Model: sentence-transformers/all-MiniLM-L6-v2 Fine-tuned using SoftmaxLoss to classify resumes → roles. Achieved strong metrics: 🧠 Weighted F1-score: 0.92 🎯 Test Accuracy: 0.81 ✅ Semantic Search (DSO2) Built a FAISS vector database for similarity retrieval. Recall@5: 0.86 | MRR: 0.80 proving high semantic match quality. ✅ Skill Graph (DSO1) Created a Neo4j knowledge graph connecting candidates, skills, and roles. Enables explainable reasoning: “Candidate 691 is a strong fit for the Machine Learning Engineer role because of Python, TensorFlow, and teamwork.” 🧠 How It Works 1️⃣ Input a job description. 2️⃣ The fine-tuned model predicts the most relevant role. 3️⃣ FAISS retrieves semantically similar resumes. 4️⃣ Neo4j identifies overlapping skills and relationships. 5️⃣ An LLM (LLaMA-3.1-8B via Ollama) explains why each candidate fits the role. All orchestrated using LangGraph + LangChain, forming a hybrid AI pipeline that combines semantic retrieval 🧬 + symbolic reasoning 🕸️ + natural language explanation 💬. 💬 Excited to push this forward and make AI recruitment more transparent and intelligent. #AI #LangChain #Neo4j #FAISS #LLM #MachineLearning #RecruitmentTech #MLOps #RAG #NLP #Innovation

With people using AI to apply for jobs and companies using AI to filter resumes. We’re entering a hiring loop where everything looks the same , perfectly written, and formatted. The only way to stand out will be to show, not tell. Build things. Put something real into the world - apps, content, open source, anything that proves you can create. In a world full of AI-generated noise, real work speaks the loudest

🔥 AI professionals, this one’s for you. After mentoring, hiring, and connecting with hundreds of machine learning and data science professionals, I noticed one big challenge: everyone prepares hard, but very few prepare right. So I decided to change that. I’ve put together The Ultimate AI Interview Q&A Workbook, a 30+ page guide packed with everything you need to break into top companies and level up your technical confidence. Here’s a glimpse of what’s inside: 💡 100+ real FAANG and top-tier company questions with clear, structured answers 💡 Hands-on coding problems with Python solutions 💡 Deep dives into ML, DL, NLP, RL, and system design 💡 Real case studies from Google, Meta, Netflix, and Tesla 💡 Behavioral, SQL, and cheat sheets for that final interview polish This isn’t theory, it’s built for practical mastery. The kind of prep that gets you noticed by hiring managers and keeps you calm under pressure. 📘 You can download it for free and start today. If you’re serious about AI growth: ✅ Follow Sivaranjan A for weekly insights, AI projects, and advanced interview prep resources 🔁 Repost to help others in your network who are preparing too 💬 Comment “AI” and I’ll make sure you don’t miss future resources #ArtificialIntelligence #MachineLearning #DataScience #DeepLearning #FAANG #InterviewPreparation #CareerGrowth #AIInterview #MLOps #NLP #LinkedInTopVoice #JobSearch #TechCareers #AICommunity #Sivaaiexpert CareerByteCode

"In particular: "curiosity, ability to learn fast and to adapt fast," Whelan, previously CEO of an AI startup sold to Apple in 2020, says." The current doom and gloom AI news cycle is riddled by this binary choice of extreme automation via AI, OR a plea to still consider the need to be curious and adapt fast. The soft stuff. Speaking of curiosity, what's not researched nearly enough is how the effective use of many AI services requires a heightened sense and discipline to be more curious than you have ever been and the very foundation of prompt engineering demands curiosity as a must-have skill. Your results are only as good as your ability to think of and construct the most curious prompts.

When AI Becomes a Crutch Instead of a Tool This morning, I spoke with a candidate for a full-stack role. When I mentioned that AI tools wouldn’t be allowed during the onsite interview, he said — “But I use AI for all my CSS.” That got me thinking. I’m not against using AI in interviews. But here’s the real question: What happens if we hire someone who can only perform with AI — but struggles to think, debug, or solve when AI gets it wrong? The cost of that hire isn’t just the salary. It’s the rework. The lost time. The hand-holding. And the realisation that we’ve built dependency right into our team. At Crescibit, we use AI daily — but we still value original thinking and technical intuition. Because AI can write code, but it can’t own outcomes. So yes, I’m okay with developers using AI. But I need to know — if AI disappeared tomorrow, could you still build, debug, and deliver? That’s the real test. #SoftwareDevelopment #CustomSoftwareDevelopment #SoftwareConsulting #development #mobileappdevelopment #ai #Crescibit

💭 What should we ask in interviews today? I recently interviewed a few junior engineers for my friend’s client 👥 Instead of asking LeetCode-style questions, I tried a new approach — and honestly, it gave us the best engineers we’ve seen 🚀 We all know today, AI can solve LeetCode problems in seconds 🤖 So why not ask questions that actually show how someone thinks and works? We asked things like: 🧠 How do you think about solving a problem? 🤖 How do you use AI tools like ChatGPT , Copilot or Cursor? ✅ How do you check if the AI’s answer is correct? 💡 How do you write clean, simple code? 💬 How do you explain your ideas to others? These simple, real questions revealed how people think, reason, and use AI effectively — not just how well they memorize algorithms. Even the junior engineers who didn’t have much experience stood out because of their clarity, logic, reasoning, and learning mindset 🌱 AI has changed how we code. It’s time interviews change too ⚡ 💬 What do you think should we still test puzzles, or focus on real-world problem solving with AI? #AI #Hiring #Tech #Interviews #FutureOfWork #SoftwareEngineering #Developers

Over the last few months, I’ve been interviewing engineers for several AI-focused roles — and it’s been an eye-opening experience. Let’s start with the image below 👇 Yes, I’ve actually seen job ads like this — asking for 5+ years of experience with LangChain or Generative AI. We all know those technologies didn’t even exist that long ago. That small example captures something we’ve noticed while hiring: the AI job market is full of confusion — between titles, tools, and actual skills. When we initially advertised roles with “AI” in the title, we were flooded with applications. Hundreds of polished CVs — many of them clearly written or enhanced by ChatGPT (which is fine). But what mattered most in interviews wasn’t how good the CV looked. It was how well the person could talk to an LLM. Because talking to a large language model is a skill. Getting meaningful, structured, and context-aware output requires clear communication, logical reasoning, and adaptability — all qualities that go far beyond “prompting”. We realised that the best candidates weren’t always those who had “AI” stamped across their résumé. So, we changed our approach. Instead of “AI Engineer” or “AI Developer”, we posted roles for Python Developers, Data Analysts, and Business Analysts. The result? Completely different league of applicants. We started meeting people who were curious, self-driven, and already exploring AI on their own — building side projects, experimenting with APIs, learning by doing. They didn’t just describe tools. They understood how to use them to solve real problems. And that’s what we’ve been looking for all along: potential, curiosity, and problem-solving. No one can have 5+ years of experience with Agentic AI, Model Context Protocol, or OpenAI SDK — because none of these existed that long. But you can always scroll to the last page of a CV — to the personal projects, the GitHub links, the small experiments — that’s where genuine learning and creativity live. So maybe the next time we evaluate AI candidates, we should look a little less at the job title… and a little more at how they think, learn, and communicate. Curious if others hiring in the AI space have seen the same pattern?

The AI job market is exploding, presenting a crucial choice for every business: scale with specialised AI talent or risk being managed by spreadsheets from 2010. By 2030, we expect 200,000 new AI roles—with wages up to 56% higher for those who master the tech. The demand is not for general admin staff, but for experts like Generative AI Engineers, Multimodal Specialists, and MLOps Pros. The winners aren't waiting; they're already building robust AI teams, scaling with Python, PyTorch, and multimodal stacks, and weaving AI into their compliance frameworks. The question is simple: Do you want to watch the AI surge or ride it? Hire smart, and secure your specialized AI talent now before the skills gap becomes permanent.