How Simera's AI, Agent Era, revolutionizes hiring with data

Ever wondered how to mock interview for your next dream IT job? Here is a step-by-step guide to make your mock interview realistic, effective, and confidence-boosting: Step 1: Define the Job Target Define your role - Software Engineer, IT Support Specialist, Cloud Architect, etc. and identify key technologies (e.g., Python, AWS, SQL, React). Make sure you understand the company type: Startup vs enterprise — interview styles differ. Step 2: Choose an Interviewer or Tool Find a friend or colleague in IT (great for feedback) or a mentor or recruiter who knows the industry. Leverage AI-based mock interview platforms (e.g., Pramp, Interviewing.io, TechMock, or even ChatGPT). Step 3: Record and Reflect After your mock, watch and rewatch your video. Ask for specific feedback: clarity, technical depth, confidence, body language and note improvement areas — e.g., “Too much jargon,” or “Need stronger examples.” Make sure you are honest with yourself about the feedback and where to improve. Step 4: Repeat with Variation Rotate between different formats (coding, design, HR) and practice with new people. Gradually add pressure simulation, like timed questions or surprise topic. Good luck with your interview prep and don't, forget to search www.veriipro.com for the latest jobs in a variety of IT sectors #CareerDevelopment #MockInterview #TechJobs #ITCareers #JobInterviewTips #CareerGrowth #InterviewPreparation #TechCareer #JobSearch #ProfessionalDevelopment #ITCommunity #CareerAdvice #ResumeTips #CareerSuccess

When was the last time you heard candidates praising a technical interview process? A $500B+ publicly-traded company just adopted our next-gen hiring process, and candidates love the experience. Our next-gen hiring process includes evaluating • Fundamentals of software engineering without an AI assistant • A real-world task on a code repo with an AI assistant in a Cursor-like IDE experience • Reviewing code written by an AI agent When you interview developers in contexts that mirror real work, you get stronger signals and a better candidate experience.

I was once headhunted by a recruiter for a software role. Normally, I’d decline, either due to a mismatch in the role or the dreaded “5 days WFO” line. But this time was different. ✔️ Remote-first role ✔️ Great pay ✔️ A fascinating role at the intersection of frontend and AI So, I decided to give it a shot. I hadn’t done structured interview prep in years, so I knew it would be a challenge. And as it turned out, the process was a firsthand glimpse into how interviews today have evolved, or rather, devolved, into a culture of question banks. My quick prep strategy was simple: - Check LeetCode, Glassdoor, Medium, and Blind for any shared interview experiences for that company. - Prepare those exact questions: I suspected it would be a question bank-style process. - Review my projects, work history, and personal initiatives. Round 1 A live LLD coding round focused on frontend implementation. As expected, the question I received was identical to one shared online. That realization was bittersweet. Without prior rehearsal, solving that within 60 minutes would have been near impossible. It almost felt like they expected candidates to find the questions, rehearse them, and then deliver polished solutions. I managed to complete most of it because I’d seen it before. Had I relied solely on intuition and problem-solving experience, it would’ve easily taken 2 hours. Round 2 Mixed DSA and JavaScript, 3 questions total. Again, all were familiar from online discussions. This time, though, I hadn’t rehearsed. I solved 2 out of 3 before time ran out, losing precious minutes debugging edge cases. Result? Rejected. None of those questions tested originality, judgment, or even creativity, things AI can’t mimic meaningfully yet. Still, I’d say it was a good experience. The rounds focused more on practical coding than obscure DSA puzzles, which is at least a small step forward. As long as timed, question-bank-style interviews dominate, this is the game candidates are forced to play. But it raises deeper questions: what kind of talent diversity are we nurturing? Are we selecting people who build, think, and design, or those who memorize, rehearse, and regurgitate? And when people use AI tools to “cheat” through these rounds, it becomes a genuine risk for companies too. Bottom line 1. If you know a company reuses specific questions, prepare them. There’s no way around it right now unless your muscle memory is exceptional. 2. Rejections happen. Don’t take them personally. Most are due to interview mismatch, not skill mismatch, especially if you already have a strong track record. 3. Interviews feel tougher today because more candidates are grinding question banks full-time. For those balancing jobs and life, it’s not an equal playing field. These rounds rarely test originality; they test preparation. A strange reality. For now, it’s the game we all have to play. If you don't want to play it, check my previous post :P

A recruiter told us last week: "I interviewed 40 candidates for a senior role. The person we hired was #38." "Why did it take so long?" "Everyone looked identical on paper. '5+ years experience.' 'Expert in Java.' 'Strong problem solver.' I had no way to know who could actually deliver until I spent hours with each of them." This is the screening bottleneck every TA team faces. When 80% of applicants look perfect on resume but only 12% have the skills you actually need - how do you find those 12% without interviewing all 40? That's exactly what Task Assessments by Charlie solves. Charlie doesn't just ask behavioral questions. Charlie gives candidates real work during the screening call: → Senior Java Engineer? Architect a microservice → DevOps Engineer? Write an automation script → QA Lead? Design scenario-based test cases The AI evaluates execution quality in real-time and delivers scored insights with detailed analytics. The transformation? Quality candidates surface in week 1. Not week 6. No more guessing based on resumes. No more wasting 40+ hours on interviews. Just a pre-qualified pipeline of candidates who've already proven they can do the work. 65% reduction in L1 screening time. Zero manual work from your team. Because credibility isn't on a resume. It's in the work itself. Curious how this works? Drop a comment or DM us. #ProductUpdate #Recruitment #TalentAcquisition #AIinHR

𝐄𝐯𝐞𝐫 𝐰𝐨𝐧𝐝𝐞𝐫𝐞𝐝 𝐰𝐡𝐲 𝐭𝐰𝐨 𝐞𝐪𝐮𝐚𝐥𝐥𝐲 𝐬𝐤𝐢𝐥𝐥𝐞𝐝 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬 𝐝𝐨𝐧’𝐭 𝐠𝐞𝐭 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐜𝐚𝐥𝐥𝐬? I reviewed 200+ developer profiles last month — and saw a pattern. The system doesn’t feel your experience. It reads your words. When recruiters search for “𝐏𝐲𝐭𝐡𝐨𝐧 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐁𝐞𝐧𝐠𝐚𝐥𝐮𝐫𝐮,” LinkedIn ranks profiles by keyword match. So, if one profile mentions Python once, and another repeats it naturally in the headline, About, and experience — guess who shows up first? “Not the better candidate — the clearer one.” Here’s the insider truth 👇 Even we recruiters get drawn to such profiles. When a skill appears consistently, it feels like you’ve worked on it deeply — before we even read your story. That’s the quiet psychology of visibility. Lesson: 𝐂𝐥𝐚𝐫𝐢𝐭𝐲 𝐛𝐞𝐚𝐭𝐬 𝐜𝐫𝐞𝐚𝐭𝐢𝐯𝐢𝐭𝐲 — 𝐫𝐞𝐩𝐞𝐚𝐭 𝐲𝐨𝐮𝐫 𝐦𝐚𝐢𝐧 𝐬𝐤𝐢𝐥𝐥 𝐧𝐚𝐭𝐮𝐫𝐚𝐥𝐥𝐲 𝐚𝐜𝐫𝐨𝐬𝐬 𝐡𝐞𝐚𝐝𝐥𝐢𝐧𝐞, 𝐀𝐛𝐨𝐮𝐭, 𝐚𝐧𝐝 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. Which skill defines your profile right now? #RecruiterInsights #JobSearch #PersonalBranding #TalentAcquisition #CareerGrowth #RecruiterLife

What are job requirements actually screening for? Most hiring bias doesn't come from malice. It comes from unexamined proxies. We write "Bachelor's degree required" when we mean "can think quantitatively." We write "5 years experience" when we mean "won't need hand-holding." We write "must know Python, SQL, R, and SAS" when we mean "can analyze data." The problem? Those proxies accidentally screen out people who have the capability but not the credential. I built a prompt to help interrogate job requirements—to spot where we're using proxies instead of measuring actual capability. It's a thought experiment as much as a practical tool. Here's how it works: You feed it a job posting. It asks: What's this requirement actually measuring? Who does it exclude? What's the minimum viable version? Is this needed on day one, or can it be learned? I deliberately left something out: The prompt stops before creating candidate evaluation criteria—because that's where most bias enters. It's one thing to write requirements. It's another to decide how to weight and evaluate candidates against them. The gap is intentional. The question it raises is the point. The prompt is in the comments in multiple parts. Copy, paste and try it on a real job posting (yours, or one you've seen). Then come back and tell me: What surprised you? What requirements were proxies you didn't realize? And if you're feeling ambitious: What evaluation criteria would YOU add to make sure you're measuring capability, not just comfort? #FutureOfWork #AIEthics #HiringBias #PromptEngineering #ResponsibleAI #DiversityAndInclusion #HRTech #WorkplaceEquity

📄 Resume Parsing Isn’t Hiring Intelligence — Here’s Why. Most ATS platforms claim to use AI — but what they actually do is data entry automation, not understanding. “Python” on a resume could mean data visualization for a marketing analyst or back-end APIs for an engineer. Traditional parsers can’t tell the difference. That’s where contextual AI changes everything. HaiTalent re-analyzes every resume per job description — interpreting skills, seniority, and role relevance. And yes, our AI is instructed to ignore PII during scoring, keeping bias in check. Recruiters spend less time filtering and more time talking to right-fit candidates. 👉 Read the full post here: https://lnkd.in/eAugUuks #AIRecruiting #TalentAcquisition #ResumeScreening #HiringTech #RecruitingSoftware

I see a lot of people adding every AI buzzword to their resume. They think it will help them stand out. It doesn't. Most hiring managers spend less than 10 seconds on a resume. They're not looking for a list of technologies. They're looking for one thing: 𝐂𝐚𝐧 𝐲𝐨𝐮 𝐬𝐨𝐥𝐯𝐞 𝐨𝐮𝐫 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬? Here's what works better: Instead of saying "I know Python and TensorFlow," show what you did with them. 1) "I used Python to clean customer data, which helped build a model that reduced support calls by 20%." 2) "I built a simple tool with TensorFlow that automatically sorted support tickets, saving the team 10 hours a week." See the difference? One is a list. The other tells a story about a problem you solved. Your IT experience is full of these stories. You just need to tell them the right way. Don't just list your skills. Show how you used them to fix something, save time, or make things better. What's a problem you solved in your last role that you're proud of?

With today's AI tools, I don't know how you can hire programmers if you don't have skilled engineer to interview them. A great technical recruiter with an engineering background will save you time and help you avoid mis-hires. Routinely I get candidate who have impressive resumes, communicate clearly, and provide written screen submissions that wow me. A significant percentage of them bomb the technical interview. They give superficial answers, or ones that sound believable but fail deeper scrutiny. If you're looking for truly senior developers, it's hard to tell these people from ones who have the kind of depth and breadth of knowledge that is needed for demanding roles. Most recruiters are not engineers and can not tell the good bluffers from the great engineers. At Vistulo, we can. Our clients don't waste time because they only get truly good candidates who were screened by an experience engineer. We repeatedly hear this feedback and are grateful for the companies who recognize this value and benefit from it.

🔥 Analytics Engineer interview stages: 1️⃣ Recruiter screening 🔍 Initial check of how well your profile matches the role. Make sure to have a good CV and prepare answers to most common questions, e.g., your motivation or salary expectations. 2️⃣ Technical screening 🧑🏻💻 Usually conducted with a hiring manager or a technical person to gauge your tech skills, like SQL, dbt, and sometimes Python (senior roles). Be prepared to talk on broader topics, like data modeling or architecting an end-to-end data pipeline. 3️⃣ Take-home assignment 📚 You'll be given a realistic data modeling challenge, where you will need to build a dbt project and solve the business questions. Make sure to explain how you will scale your project, and how it will be tested and documented. 4️⃣ Stakeholders round 🗣️ This is where you need to show your soft skills, like communication, ownership, and collaboration with others. Brush up on business metrics (e.g., with "Lean Analytics" book). 5️⃣ Offer 🎉 Polishing final details about your contract, salary, and other conditions. Congrats if you made it here!