Punit Kumar Mohanty
I’m an AI Engineer, Solution Architect & AI Operations Consultant working at the intersection of LLMs (RAG), AI Agents & Workflow Automation, DevOps/MLOps, Azure/Kubernetes, Consulting and Enablement. I build and operationalize production-grade AI platforms and internal automation systems for tax, legal, audit, production, and data teams—focusing on repeatable workflows, governance, and measurable productivity impact. I also lead trainings and workshops/hackathons to help cross-functional teams adopt AI effectively.
Portfolio
Publication
Multi Agent Deep Q-Network Approach for Online Job Shop Scheduling in Flexible Manufacturing
Published in ICMSMM 2020: International Conference on Manufacturing System and Multiple Machines at Tokyo, Japan
Talks/Presentations
Responsible and Intelligent Use of Artificial Intelligence
at Odisha University of Technology and Research, Bhubaneswar, India (Feb 2026)
Democratizing AI: Empowering the Next Generation
at United School of Business Management, Bhubaneswar, India (Feb 2026)
AI Operational Systems (Production)
LightRAG Knowledge Graph Pipeline
Description: A flexible knowledge graph ingestion and retrieval system built on LightRAG. This project enables you to ingest documents, extract entities and relationships into a knowledge graph, and perform intelligent retrieval-augmented generation (RAG) queries.
AI-supported riskanalysis from company documents
Description: I built an AI-based system for automated risk analysis that processes contracts, shareholder structures, and annual financial statements. The system extracts relevant information, answers predefined risk questions, and generates an editable risk matrix with direct source references (audit trail) to the original documents.
Used internally to automate knowledge-intensive compliance workflows that previously relied on manual SME-driven document analysis.
Measurable impact:
- Reduced manual analysis effort per case from ~6–8 hours to ~1–1.5 hours (~75% time savings)
- More consistent risk assessments through standardized question logic
- Improved traceability via automatic source linking (audit trail)
Automated LLM model benchmarking framework (Python)
Description: I developed an automated benchmarking framework in Python to systematically evaluate and compare different LLMs. The tool runs standardized test cycles (including varying model/bot parameters), produces structured rankings using defined quality metrics, and provides reusable benchmark templates so teams can configure new test cases without additional coding effort.
Measurable impact:
- Reduced manual model comparisons from multiple days to <1 hour per benchmark cycle
- Objective, reproducible evaluation instead of subjective one-off tests
- Accelerated model rollouts from weeks to a few days
Self-Service AI-platform for different departments
Description: I built backend tools and chatbot interfaces within an internal AI platform that enabled business teams to configure domain-specific document-processing agents without requiring developer support.
Measurable impact:
- Launched 30+ productive business bots within a few months
- Reduced IT support requests for recurring analyses by >60%
- Reduced implementation time for new use cases from weeks to 1–2 days
DevOps Projects
GitOps with GitHubActions
Description: This project is a practical implementation of GitOps using Terraform, Kubernetes, GitHub Actions, and Docker, AWS, EKS. It’s designed to provide hands-on experience with DevOps practices, following guidelines from “Practical GitOps” by Rohit Salecha. The project includes infrastructure setup and application deployment.
Technologies: Terraform, Kubernetes, GitHub Actions, Docker, AWS, EKS, DockerHub, GitHub Packages.