Role Overview:

We are looking for a hands-on Generative AI/ML Full Stack Developer to build, integrate, and deploy AI-powered applications. The ideal candidate will have strong Python development skills, experience building backend services and APIs, knowledge of microservices architecture, and hands-on exposure to containerization and CI/CD automation.

Key Responsibilities:

- Design, develop, and maintain scalable full-stack applications with a focus on AI/ML capabilities.

- Build backend services and REST APIs using Python, Django, and FastAPI.

- Develop and integrate microservices, ensuring reliability, performance, and maintainability.

- Integrate Generative AI and machine-learning models into application features.

- Containerize applications using Docker and support deployment across cloud and hosting environments.

- Build and maintain CI/CD pipelines, automated testing, and deployment workflows.

- Collaborate with product, design, and engineering teams to deliver secure, high-quality software.

- Troubleshoot issues across development, testing, and production environments.

- Write clean, reusable, well-documented code and participate in code reviews.

Required Skills & Experience:

- 2 - 3 years of professional software development experience.

- Strong hands-on programming experience with Python.

- Experience building applications and REST APIs using Django and/or FastAPI.

- Practical understanding of microservices architecture and service integration.

- Experience developing and deploying containerized applications using Docker.

- Knowledge of CI/CD automation, automated testing, and deployment practices.

- Understanding of frontend and backend development concepts.

- Familiarity with Generative AI/ML concepts and integrating AI/ML services into applications.

- Strong problem-solving, communication, and collaboration skills.

Preferred Qualifications:

- Exposure to cloud platforms such as AWS, Azure, or Google Cloud.

- Familiarity with Kubernetes or other container orchestration tools.

- Experience with relational or NoSQL databases.

- Understanding of application monitoring, logging, and security practices.

- Exposure to LLM APIs, prompt engineering, or Retrieval-Augmented Generation (RAG).