Nanda Kishore Joshi

About Nanda Kishore Joshi

I’m a Lead AI/ML Scientist working on large-scale, production-grade AI systems across optimization, analytics, and decision support in complex enterprise environments.Over the last 10+ years, I’ve been involved end-to-end in AI initiatives — from problem framing and data readiness to model development and operational adoption — particularly in settings where business complexity, scale, and execution constraints matter as much as technical accuracy.A recurring pattern I’ve observed is that successful AI adoption depends less on model sophistication and more on execution clarity: clear ownership, disciplined scoping, architectural judgment, and realistic expectations about what should (and should not) be built.Alongside my core role, I spend time engaging with technical leaders and early-stage founders on use-case validation and execution design — helping teams structure pilots, stress-test assumptions, and avoid common delivery pitfalls before scaling. My areas of focus include:Mathematical Optimization & Operations Research – scheduling, routing, and resource allocation problemsPredictive Analytics at Scale – decision-oriented forecasting beyond dashboardsGenerative AI & Knowledge Systems – RAG and agent-based systems for internal decision supportEnterprise Digital Twins – simulation of complex operational scenariosI value thoughtful, grounded conversations around practical AI adoption and decision systems in real-world environments. Always open to connecting with peers who are navigating complex execution challenges.

Experience

1. Lead Data Scientist

   A.P. Moller - Maersk

   Mar 2021 — Present · Bengaluru, IN

   Lead Data Scientist for Enterprise Performance Management System, driving profitability via AI/ML implementation.Pioneered Gen-AI based Autonomous Agents: Developed Insight Agent for contextual tabular data insights, self-healing Pandas/SQL agents, Hierarchical Multi-Agent systems, and a MemGPT-inspired Multi-Agentic Memory system for enhanced context and learning.Optimized LLM Deployment: Finetuned SLMs (QLoRA) and deployed on GPT-enabled VMs using vLLM/TensorRT engines with dynamic batching, reducing inference time by ~80%.Advanced RAG Implementation: Engineered sophisticated RAG techniques, including intent-based search, dynamic indexing, ranking, and Hybrid RAG with knowledge graphs.Core ML Achievements: Delivered revenue forecasts with >95% accuracy and developed a SHAPley-based Root Cause Analysis framework for data-driven business actions. Built Digital Twins of port terminals using Simpy for operational efficiency.Strategic Integration: Building frameworks to integrate Performance Management Systems with Autonomous Agents and finetuned LLMs

Education

• Visvesvaraya Technological University

  Bachelor of Engineering (B.E.), Electronics and Communications Engineering

  2011 — 2015

• International Institute of Information Technology Bangalore

  Post Graduate Diploma, Machine Learning and Artificial Intelligence

  2018 — 2019

• Liverpool John Moores University

  Master of Science - MS, Machine Learning & Artificial Intelligence

  2019 — 2020

• Indian Institute of Technology, Delhi

  Certification, Quantum Computing and Machine Learning

Skills

• Research
• Leadership
• C
• Programming
• Java
• C++
• Microsoft Word
• Html
• Customer Service
• Public Speaking
• Project Management
• Powerpoint
• Microsoft Excel
• Microsoft Office
• Management