I am a Software Engineer and M.S. Software Engineering student at Arizona State University building applied AI tools, backend/cloud systems, and Android product workflows.

I like building software that is reliable, explainable, and useful in real-world workflows. My projects connect backend services, cloud infrastructure, mobile systems, and AI-assisted automation.

I build AI-assisted tools that combine retrieval, model reasoning, validation, and structured outputs. I am interested in RAG pipelines, verifier workflows, local evaluation, model explainability, and systems where AI is used as one controlled part of a larger software workflow.

I build APIs, services, and data workflows with a focus on clean architecture, reliability, and performance. I am interested in service design, authentication, event-driven systems, API gateways, async processing, and backend workflows that are easy to test and maintain.

I work with cloud-native architectures using AWS services such as Lambda, API Gateway, DynamoDB, S3, and EC2. I am interested in serverless systems, deployment automation, infrastructure documentation, CI/CD pipelines, cost-aware design, and production-style cloud workflows.

I have experience building Android and mobile-integrated systems involving Kotlin, Jetpack Compose, Room, foreground services, background tasks, notification listeners, device-context signals, Firebase, REST backend integration, and permission-aware user flows.

Some of my current work focuses on explainability, privacy, user control, and safe automation. I am interested in systems that make intelligent decisions while avoiding unnecessary exposure of private data.

Agentic AI contract analysis system for risk detection, evidence scoring, verifier review, and clause rewrite generation.

• Built a multi-stage legal document analysis pipeline with preprocessing, local RAG, compliance checking, verifier review, weighted scoring, clause rewriting, and Markdown/JSON report generation
• Added deterministic mock-model execution and benchmark workflows for reproducible demos
• Included tests, validation scripts, and clear limitations for responsible AI use

Serverless user management and authentication system using Spring Boot and AWS.

• Built RESTful user-management APIs with Spring Boot, AWS Lambda, API Gateway, DynamoDB, and CloudFormation
• Added infrastructure-as-code templates, API documentation, deployment guide, and testing structure
• Focused on cloud-native architecture, scalability, and serverless deployment patterns

Cloud-native microservices backend for e-commerce workflows.

• Built modular services for authentication, users, products, carts, orders, orchestration, discovery, and API gateway routing
• Used Spring Boot, Kafka, Eureka, JWT, MySQL/JPA, and distributed architecture patterns
• Designed for service separation, event-driven communication, async workflows, and scalable backend design

CAR is a privacy-aware Android and Flask system designed to reduce unnecessary interruptions while preserving user control. It models user availability from mobile context and permits an automated reply only when eligibility, confidence, device-state, and safety checks pass.

• Built Android/Kotlin context collection and permission-aware workflows using Jetpack Compose, Room, DataStore, coroutines, foreground services, notification-listener coordination, and device-context readers
• Integrated participant-scoped Flask APIs and ML workflows for context ingestion, model readiness and prediction, feedback, diagnostics, and structured client responses using pandas, scikit-learn, and joblib
• Implemented fail-closed reply authorization and LLM safeguards with validation, timeouts, budgets, and safe fallbacks; verified the backend with 50 Python tests and a 360-scenario decision matrix

Custom multilingual conversational LLM training pipeline.

• Built a data pipeline for WhatsApp export parsing, cleaning, tokenization, training, evaluation, and packaging
• Implemented a custom tokenizer and lightweight transformer workflow for Hindi-English code-mixed conversational data
• Focused on privacy-filtered data handling, reproducibility, and small-model limitations

• Building applied AI systems with retrieval, verifier workflows, explainability, and structured outputs
• Building backend and cloud-native systems with stronger deployment, reliability, and observability practices
• Developing privacy-aware Android and backend ML systems with mobile context modeling, server-side LLM components, diagnostics, and user-control safeguards
• Actively interviewing for software engineering internship and new-grad opportunities across applied AI, backend systems, cloud engineering, full-stack engineering, and Android/mobile development

• Portfolio: arpit-jaiswal.vercel.app
• GitHub: arpitJ-dev
• LinkedIn: linkedin.com/in/arpitj16
• Email: arpitj.sde@gmail.com