Python & AI Developer
New
J
JobgetherSecurity & IT
Based in IndiaFull-TimeMiddle
Salary2,000,000 - 3,200,000 INR per year
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Job Details
- Experience
- 3–6 years
- Required Skills
- DockerPythonDjangoFlaskNumpyFastAPIPandasCI/CDRESTful APIs
Requirements
- 3–6 years of professional software development experience with advanced proficiency in Python.
- Hands-on experience building backend applications, RESTful APIs, microservices, or asynchronous services.
- Practical experience with modern Python frameworks such as FastAPI, Django, or Flask.
- Strong understanding of JSON, API design, relational databases, and/or NoSQL databases.
- Experience integrating machine learning models, LLMs, third-party AI APIs, or other AI-enabled technologies into software applications.
- Familiarity with data-processing and scientific Python libraries, particularly Pandas and NumPy.
- Experience developing automated tests using pytest or comparable testing frameworks.
- Working knowledge of Docker, Git, Linux environments, and fundamental cloud infrastructure concepts.
- Understanding of CI/CD principles and experience supporting automated build, testing, and deployment workflows.
- Bachelor's degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, or a related technical field.
Responsibilities
- Design, develop, and maintain robust backend services, APIs, microservices, and asynchronous task workers using Python and frameworks such as FastAPI, Django, or Flask.
- Build scalable RESTful services and backend components capable of supporting high-volume AI applications and workloads.
- Develop optimized model-serving layers for machine learning models and Large Language Models, with a focus on inference performance, reliability, and low latency.
- Integrate third-party AI services, machine learning models, and intelligent application components into production software systems.
- Design and maintain reliable ETL and data-processing pipelines for structured and unstructured data used by AI and machine learning applications.
- Use libraries such as Pandas and NumPy to support data transformation, processing, and preparation workflows.
- Write comprehensive automated tests using tools such as pytest and maintain strong standards for code quality, documentation, maintainability, and reliability.
- Containerize applications using Docker and support deployments across cloud environments through CI/CD pipelines.
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