I am an M.S. Software Engineering student at Arizona State University focused on backend engineering, cloud-native systems, Android development, and applied AI/ML workflows.

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

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 notifications, sensors, foreground services, background tasks, Firebase, REST backend integration, and user-facing workflows. I am especially interested in mobile systems that use real-world context safely and responsibly.

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.

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

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

AI-assisted 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

Research engineering contribution to an Android-based contextual auto-response system that predicts user availability and sends privacy-safe automatic replies only when eligibility, confidence, lifecycle, and user-control checks pass.

• Contributing to an Android and Flask ML workflow where incoming message notifications trigger context capture, backend availability prediction, and optional inline auto-response through Android notifications
• Worked with phone-context signals such as screen state, recent phone activity, ringer mode, notification load, calendar context, foreground app category, motion/activity, and usage state
• Supported a privacy-aware ML/LLM design where the backend decides availability first and the LLM only generates short response wording after an unavailable decision is authorized
• Helped document and test the end-to-end demo flow, permission lifecycle, notification eligibility rules, cooldown logic, backend diagnostics, and safe fallback behavior

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 backend and cloud-native systems with stronger deployment, reliability, and observability practices
• Developing context-aware Android systems that combine mobile signals, backend prediction, and privacy-safe automation
• Exploring AI-assisted developer tools, RAG pipelines, and verifier-based workflows for more reliable software systems
• Actively interviewing for software engineering internship and new-grad opportunities in backend, cloud, Android, and AI-focused engineering roles

• GitHub: arpitJ-dev
• LinkedIn: linkedin.com/in/arpitj16
• Email: arpit.jais16@gmail.com