2022 — Present
Technical Sales Director, North America @ MongoDB
Leading go-to-market architecture for AI-native workloads—federated RAG, real-time analytics, and data intelligence—on MongoDB Atlas.
AI Research · Solutions Architecture · Ph.D. Candidate
I’m Abhijit Chakraborty, Technical Sales Director at MongoDB and a Ph.D. researcher at Arizona State University. I partner with global enterprises to design production-grade knowledge graph, RAG, and multi-agent systems, while contributing research on frameworks for secure, interpretable AI.
Currently in San Francisco
Helping customers shape AI product roadmaps at scale.
Academic
Ph.D. Computer Science @ Arizona State University
CoRAL Lab, advised by Dr. Vivek Gupta · Focus on federated RAG & interpretable knowledge graphs.
Graduate
MS Computer Science @ Arizona State University
Hands-on research in multi-modal knowledge reasoning and trustworthy AI systems.
Undergraduate
B.Tech Electrical Engineering @ MAKAUT, India
Foundation in signal processing, power systems, and embedded computing.
Current Roles & Experience
Nearly two decades of experience delivering data platforms, enterprise applications, and AI solutions across Fortune 500 teams.
2015 — 2022
Principal Software Development Engineer @ Oracle
Built large-scale distributed systems, microservices, and customer analytics platforms across Oracle Cloud applications.
2014 — 2015
Software Development Engineer @ Prokarma
Partnered with Fortune 100 clients to modernize enterprise integrations and commerce experiences.
2007 — 2014
Engineering Leadership @ Infosys, SRS Consulting, TCS
Delivered data engineering and large-scale application modernization for banking, telecom, and retail enterprises.
Research Focus
Exploring the interface between structured knowledge, large language models, and responsible deployment.
Knowledge Graph Embeddings
Designing interpretable embeddings that capture semantics and enable reasoning for enterprise knowledge bases.
Retrieval-Augmented Generation
Building weighted and federated RAG systems that adapt to regulated, multi-tenant data environments.
Federated & Agentic AI Systems
Developing privacy-preserving, multi-agent orchestration for trustworthy collaboration across data silos.
Applied AI & Cloud Architectures
Leveraging MongoDB Atlas and AWS to deliver resilient LLM pipelines, real-time search, and decision intelligence.
Publications
Highlights from my research on MLOps, knowledge graphs, and federated retrieval systems.
CSCE 2024
Publication
Machine Learning Operations
Mapping Study
Machine Learning Operations: A Mapping Study
Abhijit Chakraborty, et al.
Comprehensive synthesis of how enterprises operationalize ML models—covering deployment pipelines, monitoring, governance, and reliability patterns across regulated industries.
TLDR Practitioner-focused blueprint for stable, auditable ML operations.
arXiv 2025
Preprint
RelatE
Knowledge Graph Completion
Is Architectural Complexity Overrated? Competitive and Interpretable Knowledge Graph Completion with RelatE
Abhijit Chakraborty, et al.
RelatE decomposes knowledge graph embeddings into modulus and phase components, reducing architectural complexity while preserving performance and interpretability.
TLDR Lightweight, interpretable embeddings rivaling state-of-the-art KG models.
arXiv 2025
Preprint
Federated RAG
Systematic Mapping
Federated Retrieval-Augmented Generation: A Systematic Mapping Study
Abhijit Chakraborty, et al.
Survey of architectures, privacy safeguards, and deployment practices for federated RAG across distributed, compliance-sensitive data environments.
TLDR Design patterns for secure, cross-tenant retrieval and generation.
Selected Projects
Production tooling, research prototypes, and infrastructure that power applied AI systems.
Abhijit85/ADAF
Framework
Modular pipeline for personalized, context-aware table summarization with LLM orchestration and evaluation assets.
Python
⭐ Active
GitHub →
Abhijit85/TransEE
Knowledge Graph
Interpretable knowledge graph completion with RelatE, decomposing embeddings into modulus-phase components for transparent reasoning.
Python
⭐ Active
GitHub →
Abhijit85/FederatedRAG
Federated AI
Framework combining structured relational embeddings with secure retrieval for cross-tenant knowledge sharing and auditability.
Python
⭐ Active
GitHub →
Advisory & Service
Giving back through editorial boards, open-source communities, and peer review.
Harvard Business Review
Member, Board of Advisors shaping the future of analytical leadership content.
Apache Software Foundation
Open-source contributor focused on data pipelines, governance, and AI enablement.
NeurIPS 2025 Reviewer
Reviewing submissions on trustworthy AI, federated learning, and retrieval systems.
EMNLP 2025 Reviewer
Supporting the NLP community with rigorous reviews of applied research.
Latest News
Selected highlights from the last year.
Milestones
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June 1, 2024
Paper accepted at CSCE 2024.
-
May 20, 2025
Submitted new work on federated RAG to EMNLP 2025.
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May 15, 2025
Submitted knowledge graph research to NeurIPS 2025.
Currently Exploring
Federated retrieval infrastructures for enterprise copilots.
Designing agentic workflows, governance, and observability patterns so organizations can safely scale AI assistants in regulated industries.