Rindhuja Johnson | AI Developer & Multi-Agent Systems Architect

About Me

AI Developer and Data Scientist with a Physics Background

MPS in Data Science University of Maryland - Baltimore County, USA | 4.0 GPA

M.Sc. Physics Pondicherry University, India | 3.61 GPA

I'm an AI Developer specializing in multi-agent AI systems, LLM optimization, and enterprise-grade infrastructure. I build intelligent systems that solve complex problems at scale.

My journey from theoretical physics to cutting-edge AI development has equipped me with a unique perspective on problem-solving. At UMBC, I discovered the transformative power of data during my undergraduate internship analyzing El-Niño Southern Oscillations at CUSAT.

I architect production AI systems with proven results: 7-10K token savings per session, 25-30% improvements in AI accuracy, and enterprise-scale cloud deployments. I work with cutting-edge technologies including Claude, GPT-4, Gemini, FastAPI, and GCP infrastructure to deliver measurable business value.

Core Competencies

Multi-Agent Systems LLM Optimization Prompt Engineering Cloud Architecture (GCP/AWS) FastAPI & WebSockets Token Budget Management RAG Pipelines RLHF & SFT

Kochi, India (Willing to Relocate)

Phi Kappa Phi Honor Society Member

Data Writer @ Medium

AI Expertise

Specialized in Multi-Agent Orchestration & LLM Optimization

AI Systems Architecture & Development

Built enterprise-grade multi-agent systems, LLM optimization, and cloud infrastructure

Multi-Agent Systems Design

Expertise in designing and implementing collaborative AI agent systems:

Agent Orchestration: Coordinate multiple agents for complex workflow execution
Task Routing: Intelligent request analysis and distribution to specialized agents
Data Processing: Iterative data collection with real-time progress tracking
Insight Generation: Automated synthesis of findings into actionable recommendations
Enterprise Integration: Security, compliance, and third-party system connectivity

Python FastAPI WebSockets Claude API

LLM Cost Optimization

Proven track record of optimizing AI operations for cost and efficiency:

Workflow Analysis: Identify prompt inefficiencies and excessive context retrieval
Budget Management: Implement token/cost limits with configurable enforcement
Model Selection: Optimize routing between different model tiers (Opus/Sonnet/Haiku)
Caching Strategies: Design intelligent caching for frequently-used prompts
Performance Monitoring: Track and prevent redundant API calls

Achieved 7-10K token savings per session in production

AI Workflow Orchestration

Building scalable systems for AI task management:

Parallel Processing: Execute multiple AI workflows concurrently with resource management
Smart Scheduling: Time-based task execution for optimal resource utilization
Resource Controls: Implement budget limits and usage quotas per workflow
Live Monitoring: Real-time dashboards showing progress, costs, and performance
Event Architecture: WebSocket-based communication for transparent operations

asyncio Event Streaming Task Scheduling

Cloud & DevOps

Deploying and managing enterprise-grade cloud infrastructure:

Serverless Computing: Cloud Run, Lambda with auto-scaling and cost optimization
Database Management: PostgreSQL, Redis for high-availability data storage
Infrastructure as Code: Terraform for version-controlled, reproducible deployments
Containerization: Docker for consistent environments across dev and production
Monitoring: OpenTelemetry, distributed tracing, centralized logging

GCP AWS PostgreSQL Docker Terraform

LLM Integration

Building robust multi-provider LLM applications:

API Integration: Claude, GPT-4, Gemini with unified interface
Reliability: Timeout protection, circuit breakers, and graceful degradation
Error Handling: Exponential backoff and automatic retry for transient failures
Usage Analytics: Track token consumption, costs, and performance metrics
Model Management: Dynamic routing between providers based on requirements

Anthropic API OpenAI API Gemini API LangChain

Backend Development

Building high-performance Python backend systems:

API Development: FastAPI, Flask with async/await for high concurrency
Database Layer: SQLAlchemy ORM, asyncpg for PostgreSQL, Redis for caching
Security: JWT authentication, OAuth2, password hashing (argon2)
Data Processing: Pandas, NumPy for ETL and analytics pipelines
Observability: Prometheus metrics, structured logging, distributed tracing

FastAPI SQLAlchemy Pandas JWT

Additional Expertise

LLM & Prompt Engineering

Prompt optimization techniques (5+ strategies)
RLHF initiatives & SFT implementations
Enhanced AI accuracy by 25-30%
LaTeX-formatted LLM responses

RAG & Vector Search

Retrieval-Augmented Generation pipelines
LangChain, Ollama, Chroma DB
Real-time query resolution systems
Bing Search API integrations

Data Science & Analytics

Statistical modeling & predictive analytics
Power BI, Tableau visualization
A/B testing & customer segmentation
180% engagement improvements via analytics

ML & Deep Learning

PyTorch, TensorFlow, Keras
Feature engineering & pattern analysis
95% model accuracy achievements
Time-series forecasting (SARIMAX)

Big Data & Cloud

Apache Spark, Hadoop, Databricks
GCP, AWS, Azure infrastructure
PostgreSQL, Redis, Cloud SQL
7GB+ dataset processing

Professional Experience

Building AI Systems at Scale

Lead AI Developer

Netra Systems Inc.

Aug 2025 - Present

Developed AI optimization backend achieving 7-10K token savings per session through automated workflow analysis and metadata diagnostics

Architected multi-agent orchestration system with real-time token monitoring, cost tracking, and configurable budget enforcement

Built enterprise pricing engine providing transparent model-routing and cost analysis for Claude Opus/Sonnet/Haiku

Automated cloud infrastructure deployment using Terraform, Docker, GCP Cloud Run - reducing deployment time by days

Implemented distributed backend services with FastAPI, WebSockets, JWT auth, OpenTelemetry for high-performance operations

Python FastAPI Claude API PostgreSQL Redis GCP Docker Terraform

Data Scientist

Outlier (Scale AI)

Aug 2024 - Present

Enhanced AI model accuracy by 25% through feature engineering and pattern analysis

Achieved 95% model accuracy via advanced QA frameworks and validation protocols

Improved AI-generated content accuracy by 30% using Supervised Fine-Tuning (SFT)

Led RLHF initiatives, mitigating loss categories and refining model performance

Prompt Engineering RLHF SFT QA Frameworks LLM Optimization

Junior Data Scientist

Leveragai Inc.

Aug 2024 - Jul 2025

Led development of AI Tutor for Generative AI course using Azure OpenAI

Designed RAG pipeline for real-time query resolution with LaTeX-formatted responses

Optimized prompt engineering strategies improving AI accuracy by 30%

Azure OpenAI RAG LangChain Bing Search API

Data Analyst

Redwood Algorithms

Jan 2022 - Dec 2022

Increased customer engagement by 180% through predictive analytics

Boosted ad campaign ROI by 25% through CTR analysis

Python SQL BigQuery Power BI

Featured Projects

AI, Machine Learning & Data Engineering

Featured

AI Workflow Optimization System

Open-source tool for optimizing AI developer workflows with Claude Code, Gemini, and Codex. Implements parallel orchestration, intelligent scheduling, real-time cost tracking, and automated optimization achieving 7-10K token savings per session.

Python WebSocket Multi-Agent Systems LLM APIs Cost Analytics

View on GitHub