Senior AI/ML Engineer

Job Description: Generative AI/ML Full Stack Developer

**Experience:** 2-3 years

**Employment Type:** Full-time

**Role:** Generative AI/ML Full Stack Developer

Role Overview

Were looking for a hands-on **Generative AI/ML Full Stack Developer** to build, integrate, and deploy AI-powered applications. The ideal candidate has experience developing backend services and APIs, working with microservices, and deploying containerized applications through automated CI/CD pipelines.

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.

- Work with Generative AI and machine-learning models to support application features.

- Containerize applications using tools such as **Docker** and support deployment to cloud or other hosting environments.

- Build and maintain CI/CD pipelines and 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 contribute to code reviews.

Required Qualifications

- **23 years of professional software development experience.**

- Strong hands-on experience with **Python**.

- Experience building applications and APIs with **Django** and/or **FastAPI**.

- Practical experience with **microservices architecture**.

- Experience developing and deploying **containerized applications**.

- Knowledge of **CI/CD automation** and software testing 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

- Experience with cloud platforms such as AWS, Azure, or Google Cloud.

- Familiarity with Kubernetes or other container orchestration tools.

- Experience with relational or NoSQL databases.

- Familiarity with monitoring, logging, and application security practices.

- Experience with LLM APIs, prompt engineering, or retrieval-augmented generation (RAG).