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Suman Mukherjee

Suman Mukherjee

Building the Future of Responsible AI

AI Strategist | RAG & GenAI Architect | Responsible AI Advocate

I help enterprises adopt RAG pipelines, context engineering, and multi-agent AI to transform business with accuracy and trust.

$5M+ Enterprise ROI
40% ROI Uplift
25+ Teams Led
View My Projects Let's Connect

What I Specialize In

Cutting-edge AI capabilities for enterprise transformation

RAG Pipelines

Designing retrieval-augmented assistants for factual, domain-grounded answers.

Responsible AI

Embedding governance, fairness audits, bias testing, and explainability into enterprise AI.

Prompt & Context Engineering

Optimizing prompts, memory, and multi-turn context to reduce hallucinations and improve accuracy.

Small Language Models (SLMs)

Deploying efficient AI tuned for cost, speed, and domain-specific use cases.

Multi-Agent Orchestration

Leveraging LangGraph, Autogen, and CrewAI to build collaborative, task-driven AI agents.

Enterprise AI Architecture

End-to-end pipelines: data ingestion → RAG → guardrails → monitoring → governance.

Enterprise AI in Action

Real-world impact through innovative AI solutions

Insurance

Claims Automation with RAG

Problem: Claims adjusters spent hours manually reviewing policies, leading to slow turnaround times.

Solution: Designed RAG pipeline using Azure OpenAI and LangChain, grounding LLMs in policy data.

Impact: 30% reduction in manual review effort, 2x throughput for adjusters.

Azure OpenAI LangChain Cognitive Search Databricks
Life Sciences & Pharma

AI-Driven Fraud Detection

Problem: Millions lost to fraud; rules-based system outdated.

Solution: Built ML-driven fraud detection with anomaly detection and ensemble models.

Impact: 35% fraud reduction, $2M saved annually, 20% fewer false positives.

TensorFlow AWS MLflow Python
BFSI

Enterprise AI Transformation

Problem: Fragmented AI adoption, no common governance framework.

Solution: Led enterprise-wide AI program with RAG assistants and governance guardrails.

Impact: 40% ROI uplift, 30% lead conversion increase.

Azure ML LangChain Responsible AI Databricks
Retail

Cloud Migration & Modernization

Problem: On-prem data infrastructure costly, slow, downtime-prone.

Solution: Migrated 500TB+ data to AWS Redshift with zero-downtime strategy.

Impact: $300K saved annually, enabled real-time BI adoption.

AWS Redshift Glue S3 Python

My Toolkit

Cutting-edge technologies and frameworks I work with

AI & ML

RAG Pipelines GPT-4 Claude 3.5 Llama 3 LangChain LangGraph Autogen CrewAI Hugging Face PyTorch TensorFlow Prompt Engineering

Cloud & Data

Azure OpenAI AWS SageMaker GCP Databricks Snowflake Synapse PySpark Airflow Kafka Delta Lake

Vector & Retrieval

Pinecone Weaviate FAISS Azure Cognitive Search ElasticSearch ChromaDB Hybrid Search Re-ranking Semantic Caching

MLOps & DevOps

MLflow Docker Kubernetes CI/CD Model Registry Drift Detection SHAP Prometheus Grafana Terraform

Governance

Responsible AI Model Cards Bias Audits FRMC Compliance Azure Purview Explainability Fairness Testing

Programming

Python SQL R JavaScript REST APIs GraphQL Git Power BI Streamlit

Selected Projects & Experiments

Enterprise solutions and personal innovations

BFSI

AI Transformation Program

Problem: Fragmented AI adoption, no common governance framework.

Solution: Designed enterprise-wide AI adoption program with RAG assistants and MLOps pipelines.

Impact: 40% ROI uplift, 30% lead conversion increase.

Azure ML LangChain Databricks Power BI
Life Sciences

Fraud Detection System

Problem: Millions lost to fraud; rules-based system outdated.

Solution: Built ML-driven fraud detection with anomaly detection + ensemble models.

Impact: Reduced fraud by 35%, saving $2M annually.

TensorFlow Python AWS S3 MLflow
Retail

Cloud Migration

Problem: On-prem data infra costly, slow, downtime-prone.

Solution: Migrated 500TB+ data to AWS Redshift with zero downtime.

Impact: Saved $300K annually, enabled real-time BI.

AWS Redshift Glue S3 Python
Insurance

Claims Automation with RAG

Problem: Manual claims review was slow and inconsistent.

Solution: Implemented RAG pipeline with Azure OpenAI + Cognitive Search.

Impact: Reduced manual review by 30%, doubled throughput.

Azure OpenAI LangChain Cognitive Search
Personal Project

MedScript AI

Problem: Patients struggle to read prescriptions and understand medications.

Solution: Built OCR + RAG assistant that extracts drug names and explains usage/risks.

Impact: Showcased healthcare AI potential for patient safety.

Hugging Face Tesseract OCR Pinecone Streamlit

About Me

I design AI systems that don't just answer questions — they transform enterprises. With over a decade of experience in data engineering, AI, and cloud delivery, I now focus on GenAI and Responsible AI solutions.

My expertise lies in RAG pipelines, context engineering, and AI governance, helping organizations adopt AI that is factual, ethical, and compliant.

I've led projects across BFSI, healthcare, and retail, delivering measurable outcomes: fraud detection saving $2M annually, AI adoption driving 40% ROI uplift, and cloud migrations cutting costs by $300K+.

Today, I'm exploring the frontier of SLMs, multi-agent systems, and responsible AI architectures, bringing cutting-edge innovation into real enterprise environments.

10+ Years in AI/ML
Azure Certified AI Engineer
Proven GenAI Leader
Innovating with SLMs & AI Governance

Let's Build Together

I'm always interested in discussing innovative AI projects and enterprise transformations.

Email

smn.mukherjee@gmail.com

LinkedIn

Connect on LinkedIn

GitHub

View my code