Bhargav Mitra

Background

About Bhargav Mitra

AI and Responsible AI practitioner with 18+ years of experience delivering enterprise-grade solutions across AI, Computer Vision, and, more recently, Gen-AI and Agentic-AI. I specialise in AI adoption and in taking initiatives from PoC to scalable production, ensuring measurable ROI. I advise CxOs on AI strategy, lead complex transformation programmes, and engage with analyst firms like Everest and IDC to shape market positioning. Over the last 3 years, I’ve contributed to ~$15M in new business through delivery leadership, consultative selling, and strategic client engagement. Known for an execution-focused mindset, I bring deep technical expertise combined with strong leadership and stakeholder management skills. I’m also a recognised speaker and co-author of 50+ articles on AI, Gen-AI, and Responsible AI. I’m passionate about helping organisations harness AI to solve complex problems—responsibly, at scale, and with lasting impact- Areas of Expertise: Machine learning (including Deep Learning). Generative AI (Advanced RAG-LLM architectures, Evaluation Driven Development (EDD) approach). Agentic-AI (including multi-agent orchestration, decentralised collaboration, hierarchical control). Image/video processing. MLOps. LLMOps. Responsible AI. AI adoption. Lean start-up- Industry Experience: ENU - Energy Recovery Facilities (predictive asset maintenance, Gen-AI for engineering support and contract-attributes extraction. Smart Cities (leveraging AI to improve the visual appeal of cities). Railways (predictive asset maintenance). O&G (shared services). Retail & CPG – AI/Gen-AI for marketing analytics, demand forecasting, AI driven data enrichment, and Generative BI. Media & Business Intelligence – analysis of print, radio, TV (both linear and non-linear) and social media advertisements levering AI and Computer Vision. FinTech & InsurTech – contract management, fraudulent claims detection. Pharma – effectiveness of intervention protocols for cardiovascular diseases, operationalisation of MLOps platforms-