Anubhav S.’s Post

If you're recruiting for a role related to AI, we're sharing 5 things on the blog hiring managers should know about AI roles (based on questions our clients often ask!). ⬇

#TopAIHiringTrends | Dive into insights around data science and AI talent and hiring with Burtch Works' Salary report! 💡Explore the trends shaping data and AI talent supply, demand, and compensation. From emerging roles to skill investments, get the inside scoop on where the industry is headed. Download link for full report inside! ➡️ Read now: https://hubs.ly/Q02wymJT0 #DataScience #AI #SalaryReport #TalentDevelopment #Hiring

Hey everyone! As a recruiter focused on Machine Learning Engineers, I’m always amazed by how quickly this field is evolving. I’d love to share some insights and spark a conversation with you all. 🔍 Here are a few trends I’m seeing: Explainable AI: With AI becoming integral to decision-making, transparency is key. How are organizations prioritizing explainability? MLOps: The bridge between data science and operations is becoming essential for deploying models successfully. What tools or practices have you found most helpful? Ethics in AI: As we advance, ethical considerations are more important than ever. How do you think we can ensure our models are fair and unbiased? 💡 I’d love to hear from you! What skills do you think are vital for ML engineers today? Any challenges you’re facing in your work? Let’s share our thoughts and experiences in the comments! Looking forward to connecting and learning together! 🌱 #MachineLearning #AI #Recruitment #TechTrends #MLOps #AustinTech

Next-Gen Talent Acquisition Powered by Machine Learning 🔎 Machine learning is revolutionizing talent acquisition, making it faster, smarter, and more efficient. This article from Data Science Central explores how businesses are using machine learning to streamline hiring, match candidates with roles, and improve decision-making. 👉 Read Here: https://loom.ly/m-DRxLE #MachineLearning #TalentAcquisition #Hiring #AI #Colaberry #DataScience #BusinessInnovation

Curious about AI hiring trends? Learn the 5 key questions to assess your organization’s AI needs and avoid costly mis-hires in a talent-scarce market. Dive into Angelica Chadwick's - Managing Director - Tampa - Technology Search, insights on navigating the complexities of AI & Data recruitment! https://lnkd.in/e4vpnRxX #stevendouglas #AIhiringtrends #AI #technologysearch

Amid the excitement of AI, it's important to distinguish the often blurred lines between true AI vs Automation. Working successful with my clients and hiring managers continues to require clear distinction between AI and automation to hire the right talent. Misaligned expectations of what a candidate can achieve will inevitably impact project outcomes and company goals. In my blog below I detail differences in skills/roles under AI and automation to help hire with precision, and aid candidates seeking to make the transition to AI. #AIHiring #AutomationHiring #Techhiring

AI talent can be transformative for any business, but finding the right fit takes a bit of strategy. Our latest guide breaks down the 7 essential steps for hiring top AI experts—from defining project scope and understanding specific AI roles to assessing key skills like problem-solving, collaboration, and communication. Whether you're aiming to integrate machine learning, data science, or NLP expertise, these insights will guide you toward the right hire and set your team up for AI success. Read more here 👉 #AIExperts #HiringAI #AITalent #DataScience #MachineLearning #AIConsulting #UKBusiness #TechTalent #ArtificialIntelligence #AIHiring #BusinessInnovation #TechRecruitment #AIIndustry #FutureOfWork #UKTech

Hiring for Impact: Why Responsible AI is a Key Priority for Today’s ML Teams In 2024, the world is increasingly aware that artificial intelligence isn’t just about powerful algorithms or advanced technology—it’s about real, measurable impact on society. As ML professionals, we’re all part of a field that’s evolving at unprecedented speed. But with this evolution comes responsibility, and hiring for responsible AI has become one of the most pressing issues in today’s market. When I recruit in the machine learning space, I don’t just look for exceptional technical skills. I’m searching for candidates who are passionate about the ethical dimensions of their work—professionals who care deeply about the implications of what they’re building. From safeguarding user privacy to reducing bias in data, these considerations are no longer optional. They’re crucial to creating technology that’s genuinely beneficial and respectful to its users. For example, a candidate with a strong understanding of fairness in algorithm design or a commitment to transparency can make a profound difference in shaping AI solutions that users can trust. It's about creating ML teams that don’t only solve problems but do so with accountability and integrity. As I’ve seen in conversations with clients, businesses want to hire ML professionals who are not just skilled but also grounded in principles of fairness, inclusivity, and responsibility. As thought leaders in the data and AI space, we have an opportunity—and a duty—to elevate responsible AI practices by hiring teams who will uphold these values. Let’s prioritise talent who can envision both the potential and the societal impact of their work. The future of AI depends on all of us building with a long-term vision and commitment to doing good. If you’re interested in contributing to the next wave of ethical AI development, I’d love to connect. Let’s work together to create AI solutions that we can all stand behind. #EthicalAI #DataScience Omnis Partners #MachineLearning

"Recruiters are eager to use generative AI, but a Bloomberg experiment found bias against job candidates based on their names alone" - Bloomberg For my computer science research project at Kungliga Tekniska högskolan, I'm doing an in-depth analysis of this very topic so it doesn't come as a shock to me that OpenAI's model discriminates, as I am currently testing various AI models (LLMs) for bias. What I am observing is that different models exhibit different types and levels of bias, some are more prone to show bias against ethnicity others against gender. The problem with these general purpose AI models is that they aren't suited for specific purposes but perform relatively well overall. When you try to push the model for something more specific, bias suddenly emerges that was always underlying in the training data. An example of an attempt to address this is Google's Gemini image generation, which was so "unbiased and fair" to the point that it actually became factually inaccurate (failure of world understanding). The more you push these models, the more they seem to break down. We might be facing a time of new models trained for specific purposes again, much like what was done before LLMs came and took over. #genAI #bias #womenintech #LLM #recruitment

Unlock the secrets to building a top-notch AI team with our guide on How to Hire AI Developers! Discover essential tips and strategies to find and onboard the best talent in the industry. https://lnkd.in/dtQNpu3r #ai #hiring #techtalent #howtohire #howtohireaidevelopers