Hire the Best JSTL Specialists

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Keita M.

Sanyoonoda, Japan

$20/hr
5.0
1 jobs

AI / AWS Engineer | Bedrock, RAG, Serverless, and Cloud Solutions I help businesses build practical AI and cloud solutions on AWS. My expertise includes Amazon Bedrock, RAG systems, AI agents, OpenSearch vector search, Lambda, API Gateway, DynamoDB, S3, and serverless architecture. I can support projects such as AI chatbots, document search systems, automation tools, cloud backend development, and AWS architecture improvements. I hold all AWS certifications and have hands-on experience with generative AI, machine learning, IoT systems, computer vision, and cloud-native application development. I focus on creating solutions that are reliable, scalable, and easy to maintain. Whether you need an AI prototype, an AWS serverless backend, or technical guidance for your cloud architecture, I can help you move from idea to implementation.

  • AI Builder
  • AWS Development
Siddhant M.

Pune, India

$15/hr
4.9
48 jobs

Data Engineer & AI Developer | 3+ Years Financial Industry Experience I build data pipelines, AI-powered applications, and automation systems that run reliably at scale. My background spans web scraping, LLM integration, computer vision, betting automation, and full-stack data dashboards — delivered to clients across the US, UK, Europe, and Japan. 💼 Background — 3+ years at a leading Indian bank building risk models, credit scorecards, and AutoML pipelines — PG Diploma in Big Data Analysis ⚡ What I Deliver — Web scrapers handling 1.2M+ URLs and 120K daily pipelines — LLM/AI apps using GPT-4, Gemini, LangChain, RAG, Text-to-SQL — Full Betting automation for horse racing, golf, and football signals — Computer vision pipelines with YOLOv8 and PaddleOCR — Streamlit dashboards, risk scorecards, and AutoML tools 🏆 Notable Work — PitchBook scraper — 1.2M URLs — Njuskalo — 120K daily real estate listings — Text-to-SQL architecture — BetFare — full Betfair automation — LLM Notebook — $1,420 solo delivery — Anti-bot bypass systems 🛠️ Stack Python · Playwright · Selenium · GPT-4 · Gemini · LangChain · Streamlit · PySpark · SQL · YOLOv8 · PaddleOCR · FastAPI · Betfair API · n8n Clean code. Clear communication. Delivered on time.

  • Data Analysis
  • Python
  • SQL
  • PySpark
  • Java
  • Front-End Development
  • Streamlit
  • Data Science
  • AI Chatbot
  • API
  • Web Scraping
  • Selenium
  • PyQt
  • YOLO
Swati V.

Lucknow, India

$25/hr
4.7
29 jobs

I am a full-stack software engineer with more than 8 years of experience, specializing in full stack development with a strong handle on business process and QA automation. Core competencies: ➢ AI/ML: AI Document Processing | AI Business Automation | AI Voice | AI Chatbots | AI Agents | AI Transcription | Machine Learning ➢ Automation (Selenium): Object Repository | Checkpoints | Object Identification | Descriptive Programming | Recovery Scenario Manager | Selenium WebDriver/IDE, UFT(QTP), TestNG, Junit. ➢ PHP: Laravel | Codeigniter | Yii | CorePHP | CakePHP | Drupal | WordPress ➢ Python: Django | Flask | ➢ JavaScript: React | Node | Angular | Vue | Next | Nuxt | React Native ➢ Databases: MySQL | MSSQL | DynamoDB | MongoDB ➢ Source Code Management: Git | Bitbucket | Gitlab ➢ Extensive Experience in performing manual and Automation Testing using Selenium WebDriver/IDE, UFT(QTP), Software Testing Life Cycle (STLC). ➢ Project Management: Jira | Trello | Asana Want to work together? Great! Let's open a communication channel & I will be glad to partner up with you.

  • Selenium
  • Python
  • Node.js
  • Manual Testing
  • Software Testing
  • A/B Testing
  • QA Automation
  • QA Testing
  • Deep Learning
  • Cypress
  • Automated Testing
  • React
  • AI Chatbot
  • Machine Learning
  • AI Agent Development
  • Web Content Accessibility Guidelines
  • Test Automation Framework
Nguyen Van T.

Hanoi, Vietnam

$60/hr
5.0
120 jobs

Hello, I'm Tam 👋 - 7+ years of experience in Deep Learning, Computer Vision, LLM, and Generative AI. - 3+ years of experience in AI Automation, RAG, AI Agents. - Tech stack: Python, PyTorch, TensorFlow, OpenCV, FastAPI, Docker, CUDA, AWS, Modal, DeepStream, Javascript/TypeScript, NodeJS, NextJS, ReactJS, Electron, Tauri, PyQt - Built high-performance real-time object detection systems with NVIDIA DeepStream for edge and GPU deployment. - Developed OCR & document understanding pipelines for scanned documents, engineering drawings, and forms. - Built LLM/VLM-powered AI applications, including multimodal assistants, RAG systems, image analysis, and AI inference APIs. Let's turn your AI idea into a production-ready product.

  • Deep Neural Network
  • TensorFlow
  • Computer Vision
  • PyTorch
  • Natural Language Processing
  • Deep Learning
  • Keras
  • Python
  • Machine Learning Model
  • Machine Learning
  • Data Entry
  • Docker
  • Amazon S3
  • OCR Algorithm
  • AWS Lambda
  • n8n
  • Automation
  • Selenium
Nikhil H.

Sirsi, India

$5/hr
5.0
7 jobs

I’m a backend-focused full-stack engineer with 2+ years of production experience building scalable backend systems, AI-powered applications, and high-performance APIs. My core expertise lies in backend engineering: designing reliable systems, debugging complex production issues, optimising databases, and building scalable architectures that are maintainable long-term. I work extensively with: Gen AI Solutions Python (FastAPI) Node.js Java & Spring Boot PostgreSQL AWS Redis Kafka WebSockets React I’ve built and optimised production systems involving: High-throughput backend APIs Real-time event-driven architectures PostgreSQL query optimisation & performance tuning Distributed systems & async workflows Background jobs & webhook integrations Cloud-hosted applications on AWS Microservices & scalable backend infrastructure I also specialise in GenAI and RAG-based systems, including: LLM application backend integration AI agents using LangGraph Retrieval-Augmented Generation (RAG) Vector databases (Qdrant, Pinecone) Semantic search systems Embedding pipelines Context retrieval & reranking AI workflow orchestration Production reliability for AI systems Some of my recent work includes: Building a LangGraph-based AI support assistant with multi-step reasoning and tool-calling Architecting a semantic search engine with vector search and automated ingestion pipelines Optimising backend systems with significant latency and throughput improvements Building real-time streaming systems using Kafka and WebSockets Migrating legacy systems into modern FastAPI microservices I value: Clear communication Practical engineering decisions Clean, maintainable code Reliability in production Long-term scalability over quick hacks Whether you need backend development, AI integration, RAG systems, API architecture, performance optimization, or production debugging, I can help build systems that are reliable, scalable, and production-ready.

  • Java
  • Python
  • JavaScript
  • Node.js
  • SQL
  • NoSQL Database
  • Large Language Model
  • Generative AI
  • Data Science
  • React
  • PostgreSQL
  • Generative AI Software
  • AI Chatbot
  • Retrieval Augmented Generation
  • FastAPI
  • LangChain
  • AI Agent Development
  • Vector Database
  • Microservice
Rushabh A.

Pune, India

$25/hr
5.0
3 jobs

I'm a Full-Stack Java Developer with over 8+ years of industry experience. I excel in all aspects of Java application development, from design and development to deployment. I have a proven track record of building robust and Scalable applications across diverse domains. My Expertise Full-Stack Development: Java: Core Java, multithreading, OOP, Spring, Hibernate, Struts, J2EE. Frameworks & Libraries: Spring, Hibernate, JavaFX, Apache Struts. Microservices Architecture: Spring Cloud, Netflix OSS, service discovery, load balancing, tolerance mechanisms. Web Development: JSP, Servlets, RESTful APIs, Angular, React, HTML, CSS, JavaScript. Databases: MySQL, PostgreSQL, MongoDB, Redis. Testing & Debugging: JUnit, Mockito. I also use GitHub Copilot and Claude Code to deliver the best solutions. ✔ LLM integrations (OpenAI, Claude, etc.) ✔ AI-assisted workflow automation ✔ Prompt engineering & optimization ✔ RAG based enterprise search solutions ✔ AI code acceleration using tools like Cursor ✔ Designing AI-ready Microservices architecture Java Solution Architect: Designing scalable solutions with Java, Spring, Hibernate, and microservices. DevOps: Cloud setup, infrastructure management, CI/CD. Proficient in AWS (Lambda, EC2, S3, Redshift, SageMaker, Cognito) and GCP (VMs, Google cloud storage). Domain Experience: Insurance, finance, healthcare, e-learning, e-commerce, travel, real estate, logistics, supply chain, social networking, and education. CRM and ERP systems.

  • Java
  • Spring Boot
  • Spring Batch
  • Python
  • TypeScript
  • Microservice
  • MySQL
  • PostgreSQL
  • API
  • Google Analytics
  • ETL
  • Angular
  • Data Engineering
  • Angular 5
  • React

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What does a JSTL specialist do?

A JSTL specialist builds dynamic web interfaces by implementing presentation logic in JavaServer Pages using the Jakarta Standard Tag Library. This role replaces raw Java code embedded in HTML with standardized tags that handle iteration, conditionals, and data formatting. The specialist connects backend data to frontend views through Expression Language bindings while maintaining clean separation between application logic and display layers. They refactor legacy scriptlets into maintainable tag-based structures that render correctly across different server environments.

  • Authors JSP pages that use core tag library constructs such as forEach loops and choose-when-otherwise conditionals to control content rendering based on runtime data. The specialist declares taglib directives with correct URIs and prefixes so the JSP container translates these custom actions into valid response markup without errors.
  • Binds JavaBean properties and collection data to tag attributes using Jakarta Expression Language syntax to populate dynamic elements like tables, lists, and conditional blocks. This approach eliminates the need for inline Java scriptlets and keeps the view layer focused on presentation rather than business logic processing.
  • Implements internationalization and output formatting through the fmt tag library to localize dates, numbers, and text strings according to user locale settings. The specialist configures resource bundles and applies format tags to ensure consistent display of regional data formats across the application interface.
  • Refactors existing JSP files that contain embedded Java code into standard action tags to improve readability and maintainability for future development teams. This work involves identifying scriptlet blocks that perform iteration or conditional checks and replacing them with equivalent JSTL tags that achieve the same result through declarative markup.
  • Integrates JSTL function library calls within Expression Language expressions to perform string manipulation tasks such as substring extraction, length checking, and token splitting directly in the view layer. These functions operate on page-scoped variables to transform data before it renders in the final HTML output sent to the client browser.

How to hire a JSTL specialist on Upwork

Step 1: Post a job

Define your JavaServer Pages requirements clearly to attract specialists who understand tag libraries and Expression Language. Use the Job Post Generator powered by Uma™, Upwork's Mindful AI to draft a precise description in seconds. Describe your needs in a few sentences, and Uma creates a tailored post for this role. You can write a new post, update a saved draft, or reuse an existing post.

  • Specify that the freelancer must refactor legacy scriptlets into standard JSTL actions using core and formatting tag libraries.
  • List required proficiency with Jakarta Expression Language bindings to connect JavaBeans data to JSP view components.
  • Clarify if the project involves SQL tag library usage for direct database interactions within the presentation layer.

Step 2: Evaluate candidates

Look for portfolios demonstrating clean JSP code that separates logic from presentation using standard tags. Uma runs instant video interviews and builds shortlists with side-by-side comparisons to help you assess technical fit quickly.

  • Verify experience configuring taglib directives with correct URIs and prefixes for core, fmt, and functions libraries.
  • Check for examples of internationalized output formatted via the JSTL formatting tag library for global audiences.
  • Assess their ability to implement complex iteration and conditional rendering without resorting to Java scriptlets.

Step 3: Interview your top choices

Discuss specific implementation strategies for your web application views during the interview. Schedule and conduct these conversations within Upwork Messages, which generates an immediate transcript and summary after each session.

  • Ask how they handle token operations using the JSTL functions library within Expression Language contexts.
  • Request examples of debugging tag namespace conflicts or translation errors in JSP containers.
  • Explore their approach to maintaining readability when nesting choose, when, and otherwise tags for complex logic.

Step 4: Agree on scope and begin work

Set clear milestones for JSP page updates and tag library integrations before starting. Use Upwork Messages and the contract workroom for communication and project management, plus identity verification, payment protection, hourly tracking, and project funds for security.

  • Define deliverables as refactored JSP files with verified taglib declarations and functional EL bindings.
  • Establish testing criteria to confirm the JSP container correctly interprets all custom and standard actions.
  • Agree on documentation standards for any new custom tags or function libraries added to the project.

Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.

The rates and information provided in this article are based on current data and industry sources available at the time of publication. Freelance rates can vary depending on factors such as experience, location, project scope, and market conditions. Readers are encouraged to conduct their own research to confirm current rates and trends, as this information may change over time.

How much does hiring a JSTL specialist cost?

$500-$1,500 per project is a typical range for focused JSTL specialist work. Final pricing depends on scope, technical complexity, required integrations, source-material quality, revision needs, and the freelancer's experience level.

JSP taglib configuration

$500-$1,000/project

Entry-level to mid-level
  • Configured taglib URIs and prefixes in JSP headers
  • Mapped EL expressions to JavaBean data sources
  • Verified tag namespace interpretation by the container

Scriptlet refactoring

$1,000-$2,000/project

Mid-level
  • Identified Java code blocks within existing JSP files
  • Replaced scriptlets with JSTL core tags and EL
  • Confirmed identical output after logic migration

UI logic implementation

$2,000-$4,000/project

Mid-level to senior-level
  • Built forEach loops for collection rendering
  • Applied choose and if tags for dynamic content display
  • Implemented fmt tags for date and number localization

Database integration via JSTL

$4,000-$7,500/project

Senior-level
  • Constructed SQL tags for direct database interactions
  • Bound request parameters to query placeholders safely
  • Rendered result sets using iteration tags in the view

Legacy system modernization

$7,500-$12,000/project

Expert-level
  • Assessed full application view layer for deprecated patterns
  • Executed bulk replacement of custom tags with standard JSTL
  • Tuned EL resolution and tag library loading performance

Frequently asked questions

Is hiring a JSTL specialist worth it?

For most businesses, yes: hiring a JSTL specialist is worthwhile. This expert refactors legacy JSP scriptlets into standard tag libraries to improve code maintainability and readability. They implement complex presentation logic using core and formatting tags without mixing Java code into view layers.

How do I evaluate JSTL specialist candidates?

Review their approach to separating presentation logic from business logic in JSP files. A strong candidate demonstrates clean usage of taglib directives and Expression Language bindings rather than embedding raw Java scriptlets. Ask them to explain how they handle iteration and conditional rendering using the core tag library.

What is the difference between JSTL and Jakarta Standard Tag Library?

Jakarta Standard Tag Library is the modern evolution of JSTL under the Jakarta EE specification. The functionality remains similar, but the package namespaces and URI declarations change to align with current Java enterprise standards.

Can a JSTL specialist handle database interactions in JSP pages?

Yes, specialists can use the JSTL SQL tag library to perform basic relational database queries directly within JSP pages. This approach suits simple prototypes or legacy maintenance tasks where full backend refactoring is not immediate.