Hello, I'm Rohit
"My passion lies in leveraging technology for social impact and to address real-world challenges. I thrive in entrepreneurial environments—whether building with founders, collaborating with domain experts, or shipping products that scale. I'm eager to explore new areas and make meaningful impact across healthcare, fintech, social good, or the next big thing. Let's build something that matters."
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About Me
AI-focused Software Engineer passionate about building innovative, scalable solutions.
I specialize in designing and deploying large-scale, high-performance data and AI systems at Neiman Marcus Group, supporting both customer-facing analytics and internal decision-making platforms. I'm skilled at translating complex business needs into robust technical solutions and enjoy working with cutting-edge technologies.
Prior to Neiman Marcus, I worked at NNR Global Logistics, where I tackled large-scale data challenges and contributed to modernizing pipelines across critical workflows. These experiences sharpened my understanding of software engineering best practices and the importance of building reliable, scalable systems.
I hold a Master's degree in Computer Information Technology and Management from the University of Texas at Dallas. I'm driven by a desire to push boundaries and continuously explore what's next in data engineering and AI.
Neiman Marcus Group
I design and build large-scale, high-performance data and AI-adjacent systems supporting customer-facing analytics, operational intelligence, and internal decision-making platforms. My work sits at the intersection of distributed systems, data engineering, and applied ML.
$1.5M+
Shipping Savings
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How?
Real-time Kafka streaming pipelines optimizing carrier selection & route efficiency
250%
Efficiency Gain
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Spark/Databricks automation replacing manual ETL with orchestrated data pipelines
$5M+
Annual Cost Savings
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ML-powered fraud detection & predictive analytics reducing chargebacks & losses
- Design and build large-scale data and AI-adjacent systems supporting customer-facing analytics and operational intelligence
- Translate complex business requirements into reliable, production-grade solutions operating at enterprise scale
- Partner with stakeholders across business units to translate requirements into scalable, production-grade data products
- Power downstream analytics for fraud detection, demand forecasting, and inventory optimization using ML models
University of Texas at Dallas
Projects
Projects that show real system thinking: build, ship, operate.
Nava - High-Performance Dating Platform
In Progress • App Store Launch Pending10K+
Concurrent Users
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Tokio async runtime
<50ms
P99 Latency
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WebSocket pub/sub
99.9%
Match Accuracy
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ML-powered scoring
- Rust/Axum backend engineered for 10K+ concurrent connections per node using Tokio async runtime, stateless horizontal scaling with Redis shared state, and SQLx connection pooling with PgBouncer patterns.
- GraphQL API layer with async-graphql DataLoader (N+1 elimination), field-level auth, JSONB flexible schemas, composite indexes, and read replica routing for query optimization.
- Real-time messaging via WebSocket pub/sub with broadcast channels, typing indicators, and read receipts. WebRTC signaling server for video/voice with ICE candidate relay and session state management.
Supply Chain & Inventory Analytics Platform
50+
Event Topics
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Kafka partitions
99.99%
Data Accuracy
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Exactly-once semantics
15%
Stockout Reduction
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Prophet forecasting
- Built Kafka consumer applications with manual offset commits, partition rebalancing handlers, and dead letter queues for failed message processing across 50+ inventory event topics.
- Implemented exactly-once semantics using Kafka transactions and idempotent producers for critical inventory adjustments, preventing duplicate stock updates across distribution centers.
- Developed ARIMA and Prophet forecasting models for demand prediction, integrated with Airflow DAGs for daily model retraining and feature store updates in Snowflake.
AI-Powered Order Fulfillment Platform
40%
Faster Routing
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LangGraph agents
85%
Auto-Resolution
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RAG pipelines
<100ms
API Latency
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FastAPI + DynamoDB
- Built Python FastAPI microservices for order lifecycle management with DynamoDB as primary datastore, using GSI for query patterns and DynamoDB Streams for CDC to downstream analytics systems.
- Implemented LangGraph multi-agent workflows for intelligent order routing, exception handling, and customer communication with stateful graph execution, conditional branching, and human-in-the-loop checkpoints.
- Developed LangChain RAG pipelines for customer support automation, integrating vector stores (Pinecone) for order history retrieval, semantic search across fulfillment docs, and context-aware response generation.
Interactive 3D Portfolio with AI Chatbot (This Site!)
<3s
Load Time
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CDN + lazy loading
100
Lighthouse Score
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Mobile-first design
24/7
Uptime
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AWS EC2 + PM2
- Built full-stack interactive portfolio with Next.js 16, Three.js/React Three Fiber for 3D graphics, featuring custom building model with dynamic lighting, floating tech icons, and dark/light mode adaptation.
- Implemented mobile-first responsive design optimized for iOS, Android, and iPad with hamburger navigation, touch controls, performance optimizations (lazy loading, hardware-accelerated animations, reduced particle counts on mobile), and fast load times under 3s.
- Integrated OpenAI-powered AI chatbot (Spuff) using GPT API for real-time Q&A with streaming responses, and contact form with email validation via Resend API.