Shipping ML systems at the edge of healthcare, retrieval, and reliability.
📍 New Jersey, USA · 🎯 Open to senior AI/ML roles
| 5+ yrs | 22% | 15% | 45% | 60% |
|---|---|---|---|---|
| Production AI/ML | RAG latency reduction |
Model accuracy lift |
Faster API responses |
Integration efficiency |
I'm an AI/ML engineer with 5 years of production experience building ML and Generative AI systems. Currently at Molina Health, designing risk-stratification models, RAG-driven care insights, and decision-support tools for Medicaid and Medicare populations.
Before Molina, I spent two and a half years at Cognizant building backend ML services, embedding pipelines, REST APIs on FastAPI/Flask, and the analytics plumbing that makes any of it possible. M.S. in Computer Science from SUNY Albany.
My work sits where modeling meets engineering: latency budgets, evaluation that survives production, and pipelines that don't fall over when the data changes underneath them. Offline accuracy is the start of the job — shipping is the rest.
gantt
title Industry experience
dateFormat YYYY-MM
axisFormat %Y
section Roles
Software Engineer · Cognizant :done, c1, 2020-02, 2022-08
AI / ML Engineer · Molina Health (current) :active, m1, 2023-08, 2026-05
A multi-agent retrieval-augmented system where specialized agents handle routing, retrieval, query reformulation, fact-checking, and safety. Built with LangChain + Streamlit + Groq, hybrid file/URL knowledge sources, dynamic routing and self-correction.
flowchart LR
Q([User query]) --> R{{Router agent}}
R -- "files / URLs" --> Ret[Retriever]
R -- "fresh facts" --> Web[Web search agent]
Ret --> Rf[Query reformulator]
Web --> Rf
Rf --> Fc[Fact-checker agent]
Fc --> Sc[Safety-checker agent]
Sc --> Out([Grounded response])
classDef agent fill:#1f6feb22,stroke:#1f6feb,stroke-width:1.5px,color:#1f6feb
classDef io fill:#d4ff3a22,stroke:#8ba526,color:#3d3a35
class R,Ret,Web,Rf,Fc,Sc agent
class Q,Out io
➜ github.com/ms1104n-max/multi-agentic-rag
| Repo | What it actually does |
|---|---|
multi-agentic-rag |
Multi-agent RAG with router · retriever · reformulator · web-search · fact-checker · safety agents (Streamlit + LangChain + Groq) |
rag-chatbot |
Production-aware RAG chatbot · CI/CD · M1 / NVIDIA llama.cpp · explicit cost / latency / hallucination tradeoffs |
rag-from-scratch |
RAG without framework abstractions — embeddings, local vector DB, retrieval, re-ranking, query rewriting from first principles |
langchain-rag-document-understanding |
LangChain + FAISS + SentenceTransformer pipeline for grounded document Q&A · Jupyter walkthrough |
mlops-app |
IaC reference stack — Terraform on GCP · BigQuery · GH Actions · Docker · Prefect · dbt · MLflow · FastAPI |
ai_soc— hands-on study of Srinivas et al., AI-Augmented SOC: A Survey of LLMs and Agents for Security Automation (Informatics, 2025). Implementation walkthrough used to learn multi-agent security architectures and ML-driven threat detection. Original implementation by Abdul Bari (CSU San Bernardino) — attribution preserved per Apache 2.0.
- 22% lower latency · production RAG pipelines for real-time care-management insights — Molina Health
- 15% accuracy lift · risk-adjustment and utilization-prediction models — Molina Health
- 30% faster · claims, eligibility, and provider data retrieval on AWS S3 + Snowflake — Molina Health
- 45% faster · API response times across FastAPI / Flask services — Cognizant
- 60% efficiency lift · cross-service ML integration — Cognizant
Full case studies & architecture detail at sainikhil.com →
mindmap
root((Stack))
AI and ML
Python
PyTorch
TensorFlow
XGBoost
LightGBM
Hugging Face
SHAP
Generative AI
LangChain
LlamaIndex
CrewAI
OpenAI
Anthropic
Azure OpenAI
LoRA PEFT
Data
PySpark
Pandas
NumPy
Snowflake
Postgres
MongoDB
Vector DBs
Pinecone
ChromaDB
FAISS
Cloud
AWS
Vercel
Firebase
MLOps
Docker
Kubernetes
MLflow
Weights and Biases
FastAPI
Flask
n8n
CICD
pie showData
title Focus distribution
"Generative AI · RAG · Agents" : 35
"Healthcare ML" : 30
"MLOps & Backend" : 20
"Data Engineering · Analytics" : 15
- 📍 Open to senior AI/ML and Generative-AI roles
- 🛠️ Shipping healthcare RAG and decision-support systems at Molina Health
- 📫 Reach me at [email protected]
Production-first ML — offline accuracy is the start of the job; shipping is the rest.