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Entry-level developer with production API and web app experience.

Agile
AI
Angular
C#
Python

I am a Computer Science student at the University of South Florida graduating in May 2026, with experience in software development, data analysis, machine learning, APIs, and business workflow systems. I recently worked as a Seasonal Associate Developer at Amtrust Financial Company, where I developed and shipped production APIs and web applications, resolved bugs, completed Agile user stories, and contributed ideas for improving internal workflows and user experience. Before and during my CS education, I built strong real-world business and communication experience through my role as an Insurance Underwriter/Policy Specialist at All Insurance Underwriters. In that role, I supported a CRM migration from SharePoint to Microsoft Dynamics 365, cleaned and mapped thousands of records, helped align workflows for 35 users, and worked closely with agents, clients, and internal teams to keep processes moving efficiently. My technical skills include Python, C, C++, C#, COBOL, Angular, Node.js,.NET, Git, APIs, Azure, Dynamics 365 CRM, data analysis, and machine learning. I have also completed projects involving biometric authentication, robot mapping with LiDAR and camera sensor fusion, LLM-based sentiment analysis, stock trend visualization, and social media mining around AI startup trends. I am looking for an entry-level software engineering, application developer, data/ML, QA automation, or technical analyst role where I can contribute to real products, keep improving as a developer, and work with a team that values problem-solving, clean communication, and continuous learning.

Developer's avatar

Frontend-focused full-stack developer specializing in React, APIs, and real-world SaaS systems

Recently updated
AI
Google Cloud
React
UI
UX

I’m a frontend-focused full-stack developer who got into software engineering through a background in design. I started in graphic design and UI work, which gave me a strong foundation in user experience, and then transitioned into building full applications so I could bring ideas to life end-to-end. That combination of design + engineering is a big part of how I approach development today—I care just as much about how something works as how it feels to use. Recently, I built a full-stack eCommerce platform called “Sparkle Bows” as a real-world system, not just a portfolio project. I developed a React frontend and a Node.js/Express backend with MongoDB, and integrated services like Stripe for payments and Shippo for shipping automation. I implemented JWT and OAuth authentication, built product and order management systems, and handled real-world challenges like syncing data across multiple APIs, debugging edge cases, and ensuring reliable user flows from checkout to fulfillment. That experience strengthened my ability to think through systems, not just individual features. I enjoy working on products where I can contribute across the stack—building clean, responsive UIs, integrating APIs, and making sure everything works reliably in production. I’m especially interested in roles where I can collaborate closely with product and design, ship meaningful features, and continue growing in a fast-paced environment. What sets me apart is that I don’t just write code—I think in terms of the full user journey and business impact, and I’m comfortable jumping into problems wherever they exist to get things working end-to-end.

Developer's avatar

Java Backend Developer | MS in CS

AI
AWS
Java
Python
Spring

Java Backend Engineer with 2.6 years of enterprise experience who genuinely enjoys the moment a hard problem clicks. Not just understanding it — but building something that fixes it, and watching other people benefit from that fix. That loop of learning, building, and seeing real impact is what keeps me going. My foundation is in enterprise backend systems — Spring Boot, Kafka, microservices, the kind of infrastructure where getting it wrong has real consequences. At Cognizant I worked in mobile banking, where I rebuilt a loan approval pipeline that was leaving 10,000+ users waiting four days for a decision. After redesigning it with Kafka and an embedded Python ML service, that dropped to 24 hours. I also cut AWS infrastructure costs by 40% through JVM tuning and batch job optimization — not by scaling up, but by actually diagnosing the problem. Beyond the technical depth, I've stepped into leadership as a Pod Captain — and I find just as much challenge in unblocking a team as I do in debugging a system. We all hit small friction points every day, and I genuinely enjoy turning something I've learned into a tool that removes one of them for good. What excites me most right now is applied AI and GCP — not as trends to follow, but as practical layers that make production systems smarter. I've fine-tuned BERT models, built FastAPI inference services, and integrated ML directly into production pipelines. I recently completed my Master's in Computer Science at Cal State San Bernardino with a 3.9 GPA, which deepened my conviction that the best engineering quietly makes someone's day easier. I'm based in Orlando, open to Tampa Bay opportunities, and at my best when the problem is messy enough that most people would rather avoid it.

Developer's avatar

Full-stack engineer specializing in backend systems, cloud infrastructure, and QA automation.

Automation
AWS
Data Science
Machine Learning
Python

I started my career as a software engineering intern at Jacobs Engineering Group supporting NASA projects, where I built automation tools and got my first taste of what it means to write code that actually matters. That early experience set the tone for how I approach development: build things that work reliably, automate what can be automated, and always think about the person on the other end. Since then I've worked across the full stack — from writing QA automation suites in Python at a healthcare organization to shipping production web apps with payment integrations, real-time dashboards, and cloud infrastructure at my current role. I've managed AWS deployments, built CI/CD pipelines, consolidated messy internal tooling into clean unified systems, and migrated large datasets without breaking anything in production. Beyond my day-to-day work, my deepest passion is applying machine learning to medical problems. I believe we're at an inflection point where AI has the genuine potential to save lives — catching what clinicians might miss, speeding up diagnoses, and making expert-level analysis accessible everywhere. I've been pursuing this hands-on, most recently building a 3D U-Net deep learning model to detect intracranial aneurysms in brain CT scans as part of the RSNA 2024 Kaggle competition. It's the kind of problem I want to keep solving — where getting it right isn't just good engineering, it actually matters to real patients. I'm a CS grad from UCF, based in Tampa, and always looking for problems worth solving.

Developer's avatar

AI/ML Engineer

CodeBoxx
AI
C#
DevOps
Python
React

I’m an AI and full-stack engineer with over a decade of experience building production-grade systems that operate at scale. My path into software started with distributed systems and backend engineering, and over time evolved into designing and deploying machine learning and LLM-powered products that move from research to real-world impact. I’m most energized when bridging the gap between experimentation and reliable production systems. Recently, I’ve focused on agentic AI systems—designing workflows that combine LLMs, retrieval, evaluation, and human-in-the-loop controls to solve real product problems. I’ve led the development of scalable inference services, optimized data pipelines for unstructured data, and improved model performance through rigorous experimentation and observability. Across roles, I’ve consistently worked in fast-moving environments where clarity, ownership, and engineering judgment matter more than shiny tools . What sets me apart is my ability to operate across the entire stack: from model selection and prompt design to backend services, cloud infrastructure, and user-facing integrations. I care deeply about building systems that are not only intelligent, but also reliable, maintainable, and aligned with business outcomes. I’m passionate about mentorship, thoughtful system design, and avoiding unnecessary complexity—choosing the simplest solution that actually works at scale. If you’re looking for someone who can turn ambiguous AI ideas into durable, production-ready systems, I thrive in that space.

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