Available for opportunities

Hello, I'm

Vaishnavi N

Data Scientist AI Engineer

I build production-grade AI systems with 2+ years of experience in Generative AI, RAG pipelines, and multimodal retrieval. Specializing in LLMs, vector search, and scalable ML architectures.

2+ Years Exp
8+ Projects
92% Accuracy
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Professional Profile

About Me

Data Scientist & AI Engineer specializing in production-grade intelligent systems

Executive Summary

Results-driven Data Scientist with 2+ years of experience architecting production-grade AI systems. Currently at Grid Dynamics, I specialize in Generative AI, RAG pipelines, and multimodal retrieval systems.

Proven track record in building scalable ML architectures that achieve 92%+ accuracy rates. Expert in AWS Bedrock integration, agent orchestration, and hybrid reranking strategies.

92% Retrieval Accuracy

Achieved industry-leading accuracy through advanced hybrid reranking algorithms

Production AI Systems

Architected and deployed multi-agent platforms serving enterprise clients

AWS Bedrock Integration

Expert in routing complex requests through AWS Bedrock infrastructure

Professional Journey

2023 - Present

Data Scientist

Grid Dynamics

Leading development of multi-agent platforms and MCP servers. Architecting RAG systems with 92% retrieval accuracy using hybrid reranking strategies. Integrating AWS Bedrock for complex agent workflows.

AI/ML RAG AWS Python

Technical Expertise

Specialized in cutting-edge AI technologies and scalable system architecture

Core Competencies

2+ Years
8+ Projects
95% Accuracy
95% AI/ML
85% Cloud
80% DevOps

AI & Machine Learning

Generative AI & RAG
Expert 2+ years

Production RAG systems, multimodal retrieval, LLM orchestration

NLP & Transformers
Advanced 2+ years

HuggingFace, BERT, GPT integration, custom fine-tuning

PyTorch & TensorFlow
Intermediate 1.5+ years

Model training, optimization, deployment pipelines

Backend & Cloud

Python & FastAPI
Expert 2+ years

REST APIs, async programming, microservices architecture

AWS & Azure
Advanced 1.5+ years

Bedrock, AKS, Blob Storage, serverless functions

Docker & Kubernetes
Intermediate 1+ year

Containerization, orchestration, CI/CD pipelines

Data & Tools

Vector Databases
Advanced 2+ years

Cassandra, Redis, vector search, hybrid retrieval

SQL & NoSQL
Advanced 2+ years

Complex queries, optimization, data modeling

Git & CI/CD
Expert 2+ years

Version control, automated testing, deployment pipelines

Tech Stack & Tools

Technologies I work with daily

Python
FastAPI
PyTorch
Transformers
LangChain
AWS
Azure
Docker
Kubernetes
Redis
Cassandra
Git

Featured Projects

A selection of production systems and AI solutions I've architected and deployed.

Agentas Multi-Agent Gateway Architecture Diagram

Agentas Multi-Agent Gateway

FastAPI service routing user requests through AWS Bedrock AgentCore with RAI guardrails, orchestrating multi-agent workflows with Redis session management.

AWS Bedrock FastAPI Multi-Agent Redis
Model Context Protocol Server Architecture

MCP Server Platform

Built a Model Context Protocol server exposing 9 AI tools via REST API with CSV-driven dynamic registry and Azure Blob Storage integration on AKS.

MCP Azure AKS Async
Multimodal RAG System Flow Diagram

Multimodal RAG System

End-to-end RAG system using ColPali, GPT-4o, and Cassandra achieving 92% retrieval accuracy with hybrid dense + NER reranking for text-image Q&A.

RAG ColPali GPT-4o Cassandra
TalentIQ Resume Matching Interface

TalentIQ AI Recruitment Copilot

AI-powered resume-job matching system using LLMs improving candidate fit by 40% with Streamlit UI and Firebase authentication.

LLM NLP Streamlit Firebase

My Approach

How I transform ideas into production-ready AI systems.

Research & Discovery

Deep dive into the problem space, review state-of-the-art research, and identify the optimal AI architecture.

Prototype & Validate

Build rapid prototypes with core functionality, validate assumptions through experiments, and iterate based on metrics.

Scale & Optimize

Architect for production with proper observability, optimize for latency and throughput, and implement robust error handling.

Deploy & Monitor

Deploy with CI/CD pipelines, implement comprehensive monitoring, and continuously improve based on real-world usage.

Get In Touch

Interested in collaborating on AI projects or discussing opportunities? Let's connect.