Full Stack Developer - Generative AI/Machine Learning

Job Title : Fullstack Developer GenAI, ML, AWS, Python, ReactJS

Location : Remote

Experience : 10+ Years

Job Type : Full-time

Job Summary :

We are seeking a highly experienced Fullstack Developer with deep expertise in Generative AI, Traditional Machine Learning, AWS Cloud, Python, and ReactJS. The ideal candidate will have a strong background in full-stack development, designing scalable applications, building machine learning models, and implementing GenAI capabilities into production systems.

Key Responsibilities :

- Lead the architecture, design, and development of full-stack web applications.

- Integrate Generative AI models (LLMs, transformers, embeddings, etc.) into business applications.

- Build, train, and deploy machine learning models for various predictive and classification use cases.

- Develop RESTful APIs and backend services using Python (FastAPI/Django/Flask).

- Build intuitive and dynamic front-end interfaces using ReactJS and modern JavaScript frameworks.

- Design cloud-native applications on AWS using services like Lambda, S3, EC2, API Gateway, SageMaker, and more.

- Ensure high performance, security, and scalability across the stack.

- Collaborate with cross-functional teams including Product, Data Science, and DevOps.

Required Skills & Experience :

- 10+ years of software development experience in fullstack engineering.

- Strong experience with Python for backend development and ML workflows.

- Solid knowledge and production implementation experience of GenAI models (OpenAI, Hugging Face, LangChain,

vector databases, etc.).

- Proficiency in Machine Learning algorithms and frameworks (e.g., Scikit-learn, XGBoost, TensorFlow, PyTorch).

- Expertise in ReactJS, Redux, TypeScript, JavaScript, HTML/CSS.

- Proven experience working with AWS Cloud services EC2, Lambda, S3, SageMaker, DynamoDB, CloudFormation, etc.

- Experience in CI/CD, Git, Docker, and containerized application deployment.

- Understanding of data pipelines, model serving, and MLOps best practices.