Job details

React, Node.js, Machine Learning Full Stack Developer :::: Dallas, TX (Hybrid/Onsite as required)

  • Java
  • Redux
  • REST
  • Node.js
  • GraphQL
  • Docker
  • AWS

Posted: 2 weeks ago

Job Title: React, Node.js, Machine Learning Full Stack Developer
Location: Dallas, TX (Hybrid/Onsite as required)
Experience: 6 10 years

About the Role

We are looking for a highly skilled Full Stack Developer with expertise in React.js, Node.js, and Machine Learning to join our dynamic team. The ideal candidate should have a strong background in building scalable web applications, implementing ML models into production environments, and collaborating with cross-functional teams to deliver end-to-end solutions.

Key Responsibilities

  • Design, develop, and maintain web applications using React.js (frontend) and Node.js (backend).
  • Build and integrate RESTful APIs and GraphQL services for scalable applications.
  • Collaborate with data scientists to deploy and optimize Machine Learning models in production.
  • Work with cloud platforms (AWS/Azure/Google Cloud Platform) for deployment, monitoring, and scaling solutions.
  • Ensure high performance, responsiveness, and security of applications.
  • Participate in code reviews, architecture discussions, and agile ceremonies.
  • Troubleshoot and debug technical issues across the stack.

Required Skills

  • Strong proficiency in React.js (hooks, state management, Redux/Context API).
  • Expertise in Node.js, Express.js, and REST API development.
  • Hands-on experience with Machine Learning frameworks (TensorFlow, PyTorch, or Scikit-learn).
  • Experience in integrating ML models into web applications.
  • Strong understanding of JavaScript, TypeScript, and ES6+ features.
  • Familiarity with SQL/NoSQL databases (MongoDB, PostgreSQL, MySQL).
  • Proficiency in Git, CI/CD pipelines, and containerization (Docker, Kubernetes).
  • Exposure to cloud services (AWS/Google Cloud Platform/Azure) for ML model deployment.

Preferred Qualifications

  • Experience with MLOps practices and model lifecycle management.
  • Knowledge of microservices architecture and serverless frameworks.
  • Understanding of data pipelines and ETL processes.
  • Strong problem-solving, analytical, and communication skills.