It is about creating the infrastructure that helps AI applications scale, perform reliably, and succeed in production. AI Engineers connect data, APIs, MLOps pipelines, deployment, and monitoring—turning promising prototypes into dependable platforms. The model powers the application. The AI Engineer builds the system that enables it to thrive. What is the biggest AI engineering challenge in your organisation?

🔹 NVIDIA tackles expensive AI model handoffs NVIDIA researchers introduced a cross-model KV-cache transfer technique that can reduce the compute and latency involved when agentic workflows switch between different models. This could be particularly valuable for long-running, multi-model agents where repeatedly processing large conversation histories becomes expensive. 🔹 Anthropic introduces CHIVE for investigating unexpected LLM behavior Anthropic researchers unveiled CHIVE, an agentic pipeline that discovers unusual model behaviours and tests potential explanations through counterfactual prompt experiments. Interestingly, the research found that several activation-reading interpretability tools did not outperform simply examining the model transcript for predicting these behavioural changes. 🔹 Slack brings AI coding agents into collaborative channels Slack Code brings agents including Claude Code, Devin, GitHub Copilot and Vercel's coding agent directly into Slack channels. Instead of AI coding remaining a private developer-agent interaction, teams can collectively observe, steer, review and collaborate around agent-generated work. 🔹 NVIDIA gives coding agents new skills for optimizing AI infrastructure NVIDIA has added an Agent Optimization Skillpack to its Dynamo repository, designed to help coding agents optimize AI inference deployments using techniques employed by NVIDIA engineers. It is another sign that coding agents are evolving from code-generation assistants toward specialized engineering agents capable of infrastructure and performance work. Happening today at 11AM GST: Vendor Lock-In in the AI Era: Owning the Seams + Builder Codex 📌https://nas.com/artificialintelligence/events/join-biggest-tech-community-ai-meetup-1783041366122 Happening this Today at 1:30 PM GST: MC06_ Building LLM Wrappers, Function Calls, and Data Integration 📌https://nas.com/artificialintelligence/events/nas-com-artificialintelligence-events-mc06-building-llm-wrappers-function-calls-and-data-integration

For me, it’s a simple commitment to keep learning, build practical solutions, experiment with ideas, and share what I learn along the way to the community . Looking forward to putting this into practice through small, meaningful projects.

AI projects don’t simply end at deployment—they learn, evolve, drift, and demand continuous attention. It was a privilege to speak at Decoding Data Science (DDS) Community Event on “From AI Idea to Impact: How to Successfully Manage an AI Project.” Successful AI delivery requires a shift from a traditional project mindset to a data-first, iterative, and living-system mindset—where monitoring, retraining, and MLOps are part of the journey. For project managers, this means expanding beyond scope, schedule, and budget to embrace data literacy, AI governance, ethics, explainability, and measurable business outcomes. Equally important, trust cannot be an afterthought—bias mitigation, human oversight, privacy, security, and explainability must become real project milestones with owners and dates. A huge thank you to Decoding Data Science (DDS) and @Mohammad Ahmad for giving me the opportunity and platform to share my perspective, and to everyone who joined, asked thoughtful questions, and participated in the Kahoot challenge. The journey from AI idea → POC → production → sustained impact is not just about building better models—it’s about managing AI responsibly and turning technology into lasting business value. #DecodingDataScience #DDS #ArtificialIntelligence #AIProjectManagement #AILeadership #AIGovernance #MLOps #ResponsibleAI #ProjectManagement #PMI #CPMAI #AgenticAI

Humbled and grateful to have earned 3rd Place in the Decoding Data Science Agentic AI Demo Challenge 2026 with CarePilot. For me, this recognition means more than a podium finish. It represents learning, experimenting, building, failing, improving, and having the courage to turn an AI idea into something tangible. A huge thank you to Decoding Data Science (DDS) and @Mohammad Ahmad for creating a community where we are encouraged not just to learn AI, but to build with it, challenge ourselves, and grow together. My sincere appreciation also goes to the judges, mentors, fellow finalists, and the entire DDS community for the encouragement and inspiration. I’m proud of what CarePilot achieved, but even more excited about what comes next. 3rd place is not a finish line—it’s another milestone in a much bigger learning journey. Staying humble. Staying curious. And most importantly, keeping on building. #DecodingDataScience #DDS #AgenticAI #ArtificialIntelligence #AIAgents #GenerativeAI #AIInnovation #CarePilot #ContinuousLearning #BuildInPublic #AICommunity #LearningByDoing #Gratitude #Innovation #FutureOfAI