Senior Full Stack Developer (RAG-based AI Application Development)

Job Title :

Senior Full Stack Developer (RAG-based AI Application Development)

Experience : 5-8 Years

Job Description :

We are seeking a highly skilled and experienced Senior Full Stack Developer with a strong background in developing Retrieval-Augmented Generation (RAG) based AI applications. The ideal candidate will have 5-8 years of hands-on experience in full stack development, with a proven track record of building, deploying, and maintaining advanced AI-driven solutions. You will work closely with data scientists, AI engineers, and product teams to design and implement scalable, robust, and high-performance applications that leverage the latest advancements in RAG and generative AI.

Key Responsibilities :

Design, develop, and maintain end-to-end web applications with a focus on RAG-based AI solutions.

Collaborate with AI / ML engineers to integrate retrieval and generation models into production systems.

Architect scalable backend services and APIs to support AI-driven features.

Develop intuitive and responsive front-end interfaces using modern JavaScript frameworks (e.g., React, Angular, or Vue.js).

Optimize application performance, scalability, and security.

Implement best practices for code quality, testing, and continuous integration / deployment (CI / CD).

Mentor junior developers and participate in code reviews.

Stay up-to-date with the latest trends in AI, RAG, and full stack development.

Required Skills & Qualifications :

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

5-8 years of professional experience as a Full Stack Developer.

Strong proficiency in backend technologies (e.g., Node.js, Python, Java, or Go).

Experience with frontend frameworks (e.g., React, Angular, Vue.js).

Deep understanding of RAG (Retrieval-Augmented Generation) architectures and their application in AI systems.

Hands-on experience integrating LLMs (e.g., OpenAI GPT, Llama, etc.) with retrieval mechanisms (e.g., vector databases, Elasticsearch, Pinecone, FAISS).

Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).

Experience with RESTful and GraphQL APIs.

Strong knowledge of database systems (SQL and NoSQL).

Excellent problem-solving, communication, and teamwork skills.

Preferred Qualifications :

Experience with MLOps tools and workflows.

Familiarity with data pipelines and ETL processes.

Prior experience in deploying and scaling AI / ML models in production environments.

Contributions to open-source AI or RAG projects.