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Amit Das became the AI Director at T-Series, a role that didn’t even exist a year ago. By building on his filmmaking experience and adding AI skills, he unlocked a groundbreaking position at one of the world’s biggest music labels. AI doesn’t replace your expertise—it multiplies it, creating entirely new opportunities. #TSeries #AIDirector #AppliedAI #CreativeAI #MusicIndustryInnovation
Amit Das became the AI Director at T-Series, a role that didn’t even exist a year ago. By building on his filmmaking experience and adding AI skills, he unlocked a groundbreaking position at one of the world’s biggest music labels. AI doesn’t replace your expertise—it multiplies it, creating entirely new opportunities. #TSeries #AIDirector #AppliedAI #CreativeAI #MusicIndustryInnovation
OpenAI buried the most honest sentence in AI hiring inside this posting. Success is measured by production systems, sustained adoption, and meaningful customer impact. Not activity. Not successful demonstrations. That sentence disqualifies most of what the industry has shipped for two years. The Applied AI Engineer, Enterprise role takes frontier models into banks, insurers, and compliance-heavy organizations, and makes them survive there. The requirements are refreshingly practical: strong Python, a record of taking AI systems from prototype to production, and the ability to evaluate systems systematically with representative data, graders, and production signals. No PhD. No published papers. The posting says industry experience isn’t even required. Evals are the tell. Anyone can wire a model to a workflow. The engineer who can prove the system works, week after week, with accuracy on a dashboard instead of in an anecdote, is the one enterprises renew with. If I were targeting this role, I’d build an enterprise AI deployment harness: contract intake with chunked retrieval, an extraction agent, graders scoring every output against a golden set, per-request cost and latency tracking, and a CI gate that blocks deploys when accuracy drops. The full build breakdown is on my page. Comment PROJECT for the full build. #OpenAI #AppliedAI #AIEngineer #LLMOps #EnterpriseAI
OpenAI buried the most honest sentence in AI hiring inside this posting. Success is measured by production systems, sustained adoption, and meaningful customer impact. Not activity. Not successful demonstrations. That sentence disqualifies most of what the industry has shipped for two years. The Applied AI Engineer, Enterprise role takes frontier models into banks, insurers, and compliance-heavy organizations, and makes them survive there. The requirements are refreshingly practical: strong Python, a record of taking AI systems from prototype to production, and the ability to evaluate systems systematically with representative data, graders, and production signals. No PhD. No published papers. The posting says industry experience isn’t even required. Evals are the tell. Anyone can wire a model to a workflow. The engineer who can prove the system works, week after week, with accuracy on a dashboard instead of in an anecdote, is the one enterprises renew with. If I were targeting this role, I’d build an enterprise AI deployment harness: contract intake with chunked retrieval, an extraction agent, graders scoring every output against a golden set, per-request cost and latency tracking, and a CI gate that blocks deploys when accuracy drops. The full build breakdown is on my page. Comment PROJECT for the full build. #OpenAI #AppliedAI #AIEngineer #LLMOps #EnterpriseAI
Everyone is saying “learn AI”. But most people don’t know where to start. Random videos and certificates are not enough if you don’t know how to build or apply AI in real situations. That’s why I wanted to share SlieveTech — an applied AI education company focused on practical learning. They have an 8-week online Applied AI programme starting June 6, where participants build real AI projects for GitHub, portfolio, and CV proof. But before joining anything paid, I’d suggest attending their free live AI workshop first. Dates: May 10 May 17 You’ll watch an AI app being built live in 2 hours. No AI experience needed. No coding needed to start. Free. Live. No recording. Comment AI and I’ll send you the link. #sandeepspace #AIWorkshop #LearnAI #AppliedAI #IrelandStudents IndiansInIreland CareerGrowth BuildWithAI SlieveTech
Everyone is saying “learn AI”. But most people don’t know where to start. Random videos and certificates are not enough if you don’t know how to build or apply AI in real situations. That’s why I wanted to share SlieveTech — an applied AI education company focused on practical learning. They have an 8-week online Applied AI programme starting June 6, where participants build real AI projects for GitHub, portfolio, and CV proof. But before joining anything paid, I’d suggest attending their free live AI workshop first. Dates: May 10 May 17 You’ll watch an AI app being built live in 2 hours. No AI experience needed. No coding needed to start. Free. Live. No recording. Comment AI and I’ll send you the link. #sandeepspace #AIWorkshop #LearnAI #AppliedAI #IrelandStudents IndiansInIreland CareerGrowth BuildWithAI SlieveTech
One of the most damaging governance mistakes in enterprise AI is waiting too long to involve security and legal. Teams often start with a promising use case, build a prototype, prove the model can do something useful, and build internal enthusiasm. Then, once the solution direction feels real, security and legal are brought into the conversation. That timing creates unnecessary conflict. Security is forced to react to risks that were not designed around. Legal is asked to evaluate exposure that was not bounded clearly enough. Compliance concerns arrive after the team is already emotionally and politically invested in the solution. The real problem is not that governance is blocking innovation. The problem is sequencing. Security and legal should be involved while the use case is still being shaped. They help define risk boundaries, approval paths, auditability, human review, and evidence requirements before the architecture hardens. That makes enterprise AI easier to approve, support, and scale. This video demonstrates practical use of AI and dot net tools, including avatar-based delivery and AI-generated voice narration. Explore more practical, applied enterprise AI insights at A I N Dot Net dot com. #EnterpriseAI #AIGovernance #AISecurity #AICompliance #LegalTech #ResponsibleAI #MicrosoftAI #DotNet #AIArchitecture #EnterpriseArchitecture #RiskManagement #TechLeadership #AIImplementation #DevOps #ProjectManagement #DigitalTransformation #AppliedAI #AInDotNet
One of the most damaging governance mistakes in enterprise AI is waiting too long to involve security and legal. Teams often start with a promising use case, build a prototype, prove the model can do something useful, and build internal enthusiasm. Then, once the solution direction feels real, security and legal are brought into the conversation. That timing creates unnecessary conflict. Security is forced to react to risks that were not designed around. Legal is asked to evaluate exposure that was not bounded clearly enough. Compliance concerns arrive after the team is already emotionally and politically invested in the solution. The real problem is not that governance is blocking innovation. The problem is sequencing. Security and legal should be involved while the use case is still being shaped. They help define risk boundaries, approval paths, auditability, human review, and evidence requirements before the architecture hardens. That makes enterprise AI easier to approve, support, and scale. This video demonstrates practical use of AI and dot net tools, including avatar-based delivery and AI-generated voice narration. Explore more practical, applied enterprise AI insights at A I N Dot Net dot com. #EnterpriseAI #AIGovernance #AISecurity #AICompliance #LegalTech #ResponsibleAI #MicrosoftAI #DotNet #AIArchitecture #EnterpriseArchitecture #RiskManagement #TechLeadership #AIImplementation #DevOps #ProjectManagement #DigitalTransformation #AppliedAI #AInDotNet
Stop saving articles you’ll never read. Build yourself a local knowledge base.👇 Here are the tools you need to build this system: - Obsidian Web Clipper to save my articles. - Obsidian Vault to store them in raw format. - Claude Code to read everything, organize by concept, and link ideas together automatically. The result? A queryable knowledge wiki you can actually use. Other use cases you can use it for: health research, book notes, codebase docs, meeting notes and anything else you want to learn. Comment WIKI and I’ll send you both prompts 💡 What would you build yours around? #llmwiki #claudecode #obsidian #appliedai #solutionsarchitect
Stop saving articles you’ll never read. Build yourself a local knowledge base.👇 Here are the tools you need to build this system: - Obsidian Web Clipper to save my articles. - Obsidian Vault to store them in raw format. - Claude Code to read everything, organize by concept, and link ideas together automatically. The result? A queryable knowledge wiki you can actually use. Other use cases you can use it for: health research, book notes, codebase docs, meeting notes and anything else you want to learn. Comment WIKI and I’ll send you both prompts 💡 What would you build yours around? #llmwiki #claudecode #obsidian #appliedai #solutionsarchitect
Last week, I was in Kampala. The African AI Governance Index was invited by the Government of Uganda through the Ministry of ICT and the Science, Technology and Innovation Secretariat of the Office of the President to contribute to the development of Uganda's National AI Policy. 48 experts from 18 countries. Thank you to Open Startup for building a summit worthy of the moment. To the Honorable Minister, Hon. Monica Musenero Masanza and the STI-OP team David Gonahasa, Melissa, Trevor, the quality of your engagement made clear that Uganda intends to get this right. And to every person we sat with, debated with, and built with across those two days you made the trip worthwhile. Uganda is at an inflection point. AAGI will be tracking what comes next. #AIforGood #AppliedAI #DigitalInfrastructure #GlobalSouth #Uganda DeepTech ResponsibleAI AIGovernance
Last week, I was in Kampala. The African AI Governance Index was invited by the Government of Uganda through the Ministry of ICT and the Science, Technology and Innovation Secretariat of the Office of the President to contribute to the development of Uganda's National AI Policy. 48 experts from 18 countries. Thank you to Open Startup for building a summit worthy of the moment. To the Honorable Minister, Hon. Monica Musenero Masanza and the STI-OP team David Gonahasa, Melissa, Trevor, the quality of your engagement made clear that Uganda intends to get this right. And to every person we sat with, debated with, and built with across those two days you made the trip worthwhile. Uganda is at an inflection point. AAGI will be tracking what comes next. #AIforGood #AppliedAI #DigitalInfrastructure #GlobalSouth #Uganda DeepTech ResponsibleAI AIGovernance
Most software engineers overestimate what industry ML actually is. They imagine every ML role looks like a DeepMind researcher deriving transformers on a whiteboard. That’s completely disconnected from reality. Here’s the real distinction 👇 Research ML → Invent new architectures → Publish papers → Heavy math and theory → Small percentage of total ML jobs Applied ML / Product ML → Use existing models effectively → Build pipelines and production systems → Focus on deployment, retrieval, ranking, latency, and business outcomes → Majority of industry ML roles That’s the difference most people miss. Companies rarely need you to invent the next transformer. They need you to answer practical questions like: How do we retrieve relevant documents faster? How do we reduce hallucinations? How do we serve models reliably at scale? How do we integrate embeddings into search? How do we monitor model drift? That’s engineering. And here’s the uncomfortable truth: A mediocre engineer with strong product intuition often creates more value than someone who only understands theory. Because production ML is mostly: Data pipelines Infrastructure Latency optimization Model serving Evaluation Monitoring Not deriving gradients on paper. Now don’t misunderstand this: Math still matters. But for most applied ML roles, you need: • intuition over derivation • system thinking over equations • deployment skills over paper replication If you already have strong software engineering fundamentals, you are much closer to ML than you think. The real gap is usually: • understanding ML workflows • learning how models behave in production • knowing tradeoffs between approaches Not memorizing research papers. Final takeaway 🎯 Most ML jobs are not “AI scientist” jobs. They are engineering roles that use ML as a tool. And companies pay heavily for engineers who can make ML systems actually work in production. #MachineLearning #SoftwareEngineering #AppliedAI #LLM #MLOps AIEngineering DataScience GenerativeAI TechCareers ProductEngineering MLSystems ArtificialIntelligence CareerGrowth Engineering MLCareer
Most software engineers overestimate what industry ML actually is. They imagine every ML role looks like a DeepMind researcher deriving transformers on a whiteboard. That’s completely disconnected from reality. Here’s the real distinction 👇 Research ML → Invent new architectures → Publish papers → Heavy math and theory → Small percentage of total ML jobs Applied ML / Product ML → Use existing models effectively → Build pipelines and production systems → Focus on deployment, retrieval, ranking, latency, and business outcomes → Majority of industry ML roles That’s the difference most people miss. Companies rarely need you to invent the next transformer. They need you to answer practical questions like: How do we retrieve relevant documents faster? How do we reduce hallucinations? How do we serve models reliably at scale? How do we integrate embeddings into search? How do we monitor model drift? That’s engineering. And here’s the uncomfortable truth: A mediocre engineer with strong product intuition often creates more value than someone who only understands theory. Because production ML is mostly: Data pipelines Infrastructure Latency optimization Model serving Evaluation Monitoring Not deriving gradients on paper. Now don’t misunderstand this: Math still matters. But for most applied ML roles, you need: • intuition over derivation • system thinking over equations • deployment skills over paper replication If you already have strong software engineering fundamentals, you are much closer to ML than you think. The real gap is usually: • understanding ML workflows • learning how models behave in production • knowing tradeoffs between approaches Not memorizing research papers. Final takeaway 🎯 Most ML jobs are not “AI scientist” jobs. They are engineering roles that use ML as a tool. And companies pay heavily for engineers who can make ML systems actually work in production. #MachineLearning #SoftwareEngineering #AppliedAI #LLM #MLOps AIEngineering DataScience GenerativeAI TechCareers ProductEngineering MLSystems ArtificialIntelligence CareerGrowth Engineering MLCareer
Scholarships up to 100% – Based on Performance 🔗 Link in Bio to Apply 📌 12th ke baad AI & Data Analytics mein career banana chahte ho? Ye naya programme check karo 👇 ✅ 4-Year BS (Hons) in Data Analytics & Applied AI ✅ By JAIN School of Global Studies, in academic partnership with UCAM University, Spain ✅ Real-world projects + internships from Year 1 ✅ Build actual AI systems, not just theory ✅ Learn directly from industry mentors ✅ International exposure & global learning pathways ✅ Give the Global Futures Scholarship Test— score well, get up to 100% scholarship 💡 Aaj sirf degree kaafi nahi — companies skills, projects aur real experience dekhti hain. Ye programme aapko wahi deta hai. 👉 Interested? Details & test registration — link in bio #DataAnalytics #AppliedAI #UCAMUniversity #scholarshiptest #careerinai
Scholarships up to 100% – Based on Performance 🔗 Link in Bio to Apply 📌 12th ke baad AI & Data Analytics mein career banana chahte ho? Ye naya programme check karo 👇 ✅ 4-Year BS (Hons) in Data Analytics & Applied AI ✅ By JAIN School of Global Studies, in academic partnership with UCAM University, Spain ✅ Real-world projects + internships from Year 1 ✅ Build actual AI systems, not just theory ✅ Learn directly from industry mentors ✅ International exposure & global learning pathways ✅ Give the Global Futures Scholarship Test— score well, get up to 100% scholarship 💡 Aaj sirf degree kaafi nahi — companies skills, projects aur real experience dekhti hain. Ye programme aapko wahi deta hai. 👉 Interested? Details & test registration — link in bio #DataAnalytics #AppliedAI #UCAMUniversity #scholarshiptest #careerinai
What if AI gave you more time, not because there is less work, but because you are finally working less on the wrong things. That is the question I keep coming back to. Not “can AI write my emails” but “can AI give me and my team a better quality of life.” Here is the rule I use with every client: AI does 80% of the work. The last mile stays human. That 80/20 split is where the magic is. AI is not there to replace you, it is there to buy back your time so you can show up for the 20% that actually needs you. This is what I mean when I say Applied AI, or AI for Business. Not theory. Not hype. A real system that gives you your time back. If you are curious what that could look like for you and your team, let’s talk. DM me “AI” and let’s talk about what this could look like for your business. #appliedai #aiforbusiness #entrepreneur #qualityoflife #businessgrowth
What if AI gave you more time, not because there is less work, but because you are finally working less on the wrong things. That is the question I keep coming back to. Not “can AI write my emails” but “can AI give me and my team a better quality of life.” Here is the rule I use with every client: AI does 80% of the work. The last mile stays human. That 80/20 split is where the magic is. AI is not there to replace you, it is there to buy back your time so you can show up for the 20% that actually needs you. This is what I mean when I say Applied AI, or AI for Business. Not theory. Not hype. A real system that gives you your time back. If you are curious what that could look like for you and your team, let’s talk. DM me “AI” and let’s talk about what this could look like for your business. #appliedai #aiforbusiness #entrepreneur #qualityoflife #businessgrowth
1 year ago we ran our first workshop 🎉 150 people walked in not knowing if they could build an AI system. That’s one year of Genaition. No brand, no playbook. Just founders and operators who were tired of reading about AI and wanted to actually use it. From email agents handling outreach to chatbot systems running 24/7, workflows that used to take hours, done in an afternoon. Year two starts now. If you’ve been waiting to start, stop waiting. June 13th, online 3 hours. Walk away with a working agent. September, Stockholm, 4 weeks. Walk away with AI team and invaluable knowledge how to build and manage it. Links in bio. #Genaition #AIAgents #AppliedAI #StockholmStartups #Nocode
1 year ago we ran our first workshop 🎉 150 people walked in not knowing if they could build an AI system. That’s one year of Genaition. No brand, no playbook. Just founders and operators who were tired of reading about AI and wanted to actually use it. From email agents handling outreach to chatbot systems running 24/7, workflows that used to take hours, done in an afternoon. Year two starts now. If you’ve been waiting to start, stop waiting. June 13th, online 3 hours. Walk away with a working agent. September, Stockholm, 4 weeks. Walk away with AI team and invaluable knowledge how to build and manage it. Links in bio. #Genaition #AIAgents #AppliedAI #StockholmStartups #Nocode
The best AI companies don’t build from HQ. They build inside the customer. The Forward-Deployed AI Forum w/ Shilpi Nayak @ House of 434 + Tavern Ravin Thambapillai [Credal.ai / ex-Palantir] on how bespoke deployments become defensible platforms. My Q: If energy is blocking AI, is uranium the future? And DeepSeek? My take: You can distill cheap. You can’t distill the frontier. I’m George Stephan — inside NYC rooms where applied AI actually ships. Follow for the debrief you missed. #ForwardDeployedAI #EnterpriseAI #NYCTech @shilpinkk @taverncommunity @ravintpillai
The best AI companies don’t build from HQ. They build inside the customer. The Forward-Deployed AI Forum w/ Shilpi Nayak @ House of 434 + Tavern Ravin Thambapillai [Credal.ai / ex-Palantir] on how bespoke deployments become defensible platforms. My Q: If energy is blocking AI, is uranium the future? And DeepSeek? My take: You can distill cheap. You can’t distill the frontier. I’m George Stephan — inside NYC rooms where applied AI actually ships. Follow for the debrief you missed. #ForwardDeployedAI #EnterpriseAI #NYCTech @shilpinkk @taverncommunity @ravintpillai
Everyone is saying “learn AI” . But most people don’t know where to start. Random YouTube videos and certificates are not enough if you don’t know how real AI tools are built. That’s why I wanted to share this. @slievetech has an 8-week online Applied AI programme starting June 6, where participants build real AI projects for GitHub, portfolio, and CV proof. But before spending any money, attend their free live AI workshop first. Free live workshops: May 10 May 17 You’ll watch an AI app being built live in 2 hours. No AI experience needed. No coding needed to start. Free. Live. No recording. Useful for students, graduates, career switchers, and working professionals who want to understand AI practically. Comment AI and I’ll send you the link. Register here: https://www.slievetech.com/workshops #AIWorkshop #LearnAI #AppliedAI #IndiansInIreland #TeluguInIreland
Everyone is saying “learn AI” . But most people don’t know where to start. Random YouTube videos and certificates are not enough if you don’t know how real AI tools are built. That’s why I wanted to share this. @slievetech has an 8-week online Applied AI programme starting June 6, where participants build real AI projects for GitHub, portfolio, and CV proof. But before spending any money, attend their free live AI workshop first. Free live workshops: May 10 May 17 You’ll watch an AI app being built live in 2 hours. No AI experience needed. No coding needed to start. Free. Live. No recording. Useful for students, graduates, career switchers, and working professionals who want to understand AI practically. Comment AI and I’ll send you the link. Register here: https://www.slievetech.com/workshops #AIWorkshop #LearnAI #AppliedAI #IndiansInIreland #TeluguInIreland
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