Full Stack Engineer: Python, React & ML/NLP in the Cloud

Technopride Ltd; Polska Cerekiew; On-site; PLN 120,000 - 210,000; Full time

Job summary

Technopride Ltd. seeks a skilled Full Stack Engineer to join the Data Analytics team. You will design and implement end-to-end applications using Python (FastAPI) and React, delivering production-grade ML/NLP solutions and scalable APIs.

The role involves cloud deployments (AWS or similar), Docker, CI/CD, and collaboration with data scientists to optimize data pipelines and model performance.

Qualifications

• Proven experience as a Full Stack Developer with Python, FastAPI, and React.
• Hands-on experience with ML/NLP applications and model inference APIs.
• Strong understanding of data processing, API design, and distributed systems.
• Experience with cloud deployments (AWS or other) and Docker, CI/CD pipelines.
• Knowledge of software engineering fundamentals, Git, and Agile methodologies.

Responsibilities

• Design, develop, and maintain end-to-end applications using Python (FastAPI) for backend and React for frontend.
• Build and deploy ML/NLP-driven products, integrating machine learning models into production systems.
• Develop scalable APIs for data processing, model inference, and analytics workflows.
• Implement and manage cloud deployments using AWS or equivalent platforms and containerization.
• Collaborate with data scientists and engineers to optimize data pipelines and model performance.
• Ensure scalability, performance, and maintainability through best coding practices and DevOps tools.

Skills

Python

FastAPI

React

ML/NLP

APIs

Cloud deployments

Docker

CI/CD

Git

Agile

Tools

Docker

CI/CD

Job description

Technopride Ltd. seeks a skilled Full Stack Engineer to join the Data Analytics team. You will design and implement end-to-end applications using Python (FastAPI) and React, delivering production-grade ML/NLP solutions and scalable APIs.

The role involves cloud deployments (AWS or similar), Docker, CI/CD, and collaboration with data scientists to optimize data pipelines and model performance.