Full - Stack Developer - AI

We are looking for an experienced AI Full Stack Developer with strong expertise in React, Python, AI/ML integration, vector databases, and cloud-native deployment technologies. The ideal candidate will build scalable web applications integrated with AI capabilities, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and intelligent search solutions. The candidate should have hands-on experience across frontend and backend development, AI application integration, containerization, CI/CD, and MLOps.

Responsibilities:

• Develop responsive and scalable frontend applications using React.js, JavaScript, and TypeScript.
• Build robust backend services and APIs using Python and frameworks such as FastAPI, Flask, or Django.
• Integrate LLMs and generative AI capabilities into web applications.
• Develop AI-powered solutions using RAG, embeddings, semantic search, and vector databases.
• Work with vector databases such as Pinecone, Qdrant, Weaviate, Milvus, or Chroma.
• Integrate AI models and external AI services through APIs.
• Containerize applications using Docker and deploy them using Kubernetes.
• Build and maintain CI/CD pipelines for automated testing and deployment.
• Implement MLOps practices for model deployment, versioning, monitoring, and lifecycle management.
• Collaborate with AI/ML engineers, product teams, and other developers to deliver production-ready AI applications.
• Optimize application performance, scalability, security, and reliability.

Requirements:

• We are looking for a hands-on engineer who can build full-stack applications and integrate AI/GenAI capabilities into production systems.
• The candidate should understand the complete lifecycle of AI-powered applications, from frontend development and backend APIs to vector search, model integration, deployment, and monitoring.

Required Skills:

• Frontend: React.js, JavaScript, TypeScript, HTML, CSS.
• Backend: Python, FastAPI, Flask, or Django.
• AI/Generative AI: LLMs, generative AI integration, RAG, embeddings, prompt engineering, and semantic search.
• Vector Databases: Pinecone, Qdrant, Weaviate, Milvus, or Chroma.
• API Development: REST APIs, third-party integrations, authentication.
• Containerization: Docker.
• Orchestration: Kubernetes.
• CI/CD: Jenkins, GitHub Actions, GitLab CI/CD, or equivalent tools.
• MLOps: Model deployment, monitoring, versioning, and ML pipeline management.
• Databases: PostgreSQL, MongoDB, MySQL, or equivalent.
• Version Control: Git and collaborative development workflows.

Good to Have:

• Experience building AI agents and agentic AI workflows.
• Familiarity with LangChain, LangGraph, or LlamaIndex.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Knowledge of AI model serving, evaluation, and performance optimization.
• Experience with open-source LLMs and model deployment.
• Familiarity with PyTorch, TensorFlow, or Hugging Face.