Full Stack AI Developer
J
JobgetherTechnology and Healthcare
Based in IndiaFull-Time
Salary not disclosed
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Job Details
- Required Skills
- PythonCloud ComputingETLFull Stack DevelopmentJavaMachine LearningC#Data engineeringRESTful APIs
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.
- Strong programming skills in at least one modern programming language such as Python, Java, or C#.
- Experience developing software services using APIs, databases, and version control systems.
- Solid understanding of full-stack development principles and software engineering best practices.
- Experience working with AI or machine learning frameworks and developing production-ready ML workflows is preferred.
- Familiarity with cloud platforms, containerization technologies, and modern deployment practices.
- Experience with data engineering concepts, including ETL/ELT processes, data quality management, and performance optimization.
- Strong problem-solving abilities with attention to code quality, scalability, and system reliability.
- Ability to collaborate effectively with cross-functional teams and communicate technical concepts clearly.
- A continuous learning mindset and interest in emerging AI technologies.
Responsibilities
- Design, develop, test, and maintain full-stack applications and AI-enabled services using modern software engineering practices.
- Build and optimize data pipelines, machine learning workflows, and backend services to support AI-driven capabilities.
- Translate product requirements into scalable technical solutions in collaboration with product and engineering stakeholders.
- Develop clean, efficient, and maintainable code while following engineering best practices for quality, security, and performance.
- Create and integrate APIs, databases, and software components to deliver robust application functionality.
- Monitor deployed systems, troubleshoot technical issues, and improve solutions based on performance metrics and user feedback.
- Contribute to engineering standards, documentation, and continuous improvement initiatives.
- Support the development and deployment of machine learning models and AI workflows in production environments.
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