Vaibhav Simikeri

About Vaibhav Simikeri

I have spent 17 years building and scaling AI systems in production — personalization platforms, fraud detection systems, and enterprise ML platforms processing 500K+ daily requests across regulated industries. Over that time, my focus has shifted upstream to the question that sits behind all of it: how do you make AI governance executable at enterprise scale — embedded into engineering workflows rather than enforced through documents and checklists?I call this Executable Governance, and it is a conviction built through practice, not theory. I have architected governance frameworks across 50+ AI systems in banking, pharma, manufacturing, and travel, reducing compliance cycles by 60% while preserving delivery speed and engineering autonomy. The principle is consistent: when governance is part of the system, teams move faster with fewer surprises. When it is treated as an external function, friction and risk compound quietly.I work across three modes:As a leader — building and scaling global AI engineering organizations, establishing AI governance Centers of Excellence, and aligning engineering, product, legal, and executive stakeholders around shared accountability for AI systems that scale and withstand regulation.As a practitioner — staying close to the engineering reality of GenAI, agentic systems, and production ML, because governance frameworks that are disconnected from how systems are actually built do not survive contact with scale.As an advisor — helping senior leaders and boards navigate AI governance strategy, EU AI Act implementation, and the practical challenge of making regulation executable across regulated industries.If you are responsible for AI systems in a high-stakes environment and are working to make governance practical rather than performative, I welcome the conversation.Focus:Enterprise AI platforms, AI governance & EU AI Act compliance, GenAI & LLM systems (RAG, agentic architectures), MLOps | Python, Cloud (IBM Cloud, AWS)Industries: Banking & Financial Services, Pharma & Healthcare, Manufacturing, Travel & Ecommerce

Experience

1. Ai Engineering Leader

   IBM

   Sep 2023 — Present

Education

• Visvesvaraya Technological University

  Bachelor of Engineering (B.E.), Information Technology

• Birla Institute of Technology And Science (BITS), Pilani

  Master of Technology - M. Tech, Artificial Intelligence and Machine Learning

Skills

• Data Science
• Java
• Architecture
• Hadoop
• Selenium
• Agile Methodologies
• Test Automation
• Web Services
• Big Data
• Apache Spark
• Apache Pig
• Flume
• Mapreduce
• Data Mining
• Requirements Analysis
• Jenkins
• Python
• Jmeter
• Machine Learning
• Performance Testing
• Big Data Analytics
• Hive
• Oozie