Senior Full Stack AI Engineer

Job Description This is a remote position. Job Description: Senior Full Stack AI Engineer (8+ YoE) Role: Senior Full Stack AI Engineer Experience: 8+ years in software engineering, including 3+ years of hands-on full stack product development Role Type: Professional IT Engineering Role Role Overview We are seeking a highly skilled and experienced Senior Full Stack AI Engineer to design, develop, and deliver scalable, secure, and intelligent software products. The ideal candidate will have strong expertise across frontend and backend technologies, hands-on experience in end-to-end product development, and practical exposure to AI-driven applications such as Retrieval-Augmented Generation (RAG), chatbots, Large Language Models (LLMs), and prompt engineering. This role requires a professional IT engineer who can work across the full software development lifecycle, collaborate with cross-functional teams, and build production ready AI-enabled solutions on modern cloud platforms such as AWS, Google Cloud Platform (GCP), and Microsoft Azure. Key Responsibilities • Design, develop, and maintain end-to-end web applications using modern frontend and backend technologies. • Build responsive, high-performance user interfaces using React JS and related JavaScript or TypeScript frameworks. • Develop scalable backend services, RESTful APIs, and microservices using Node.js, Django, FastAPI, Flask, or similar frameworks. • Own full product development lifecycle activities including requirements analysis, architecture design, implementation, testing, deployment, monitoring, and continuous improvement. • Integrate AI capabilities into enterprise applications using LLMs, RAG pipelines, chatbot frameworks, and prompt engineering techniques. • Design and implement AI-enabled workflows including document ingestion, embeddings, vector search, retrieval optimization, and response generation. • Collaborate with product managers, UX designers, AI/ML engineers, DevOps teams, and business stakeholders to deliver reliable and user-focused solutions. • Ensure application security, scalability, performance, maintainability, and reliability across frontend, backend, database, and AI components. • Deploy, manage, and optimize applications and AI services on cloud platforms such as AWS, Google Cloud Platform (GCP), and Microsoft Azure. • Write clean, modular, well-tested, and maintainable code following software engineering best practices. • Mentor junior engineers, participate in code reviews, and contribute to technical design discussions and architecture decisions. Required Qualifications • 8+ years of overall professional experience in software engineering or full stack application development. • Minimum 3+ years of hands-on experience in end-to-end software product development using frontend and backend technologies. • Strong frontend development experience with React JS, JavaScript, TypeScript, HTML, CSS, and modern UI development practices. • Strong backend development experience with Node.js and Python-based frameworks such as Django, FastAPI, and Flask. • Experience designing and consuming REST APIs, integrating third-party services, and developing secure backend systems. • Hands-on experience with databases such as PostgreSQL, MySQL, MongoDB, Redis, or similar SQL and NoSQL technologies. • Practical experience in building or integrating AI-powered solutions using LLMs, RAG, chatbots, and prompt engineering. • Good understanding of software architecture, system design, debugging, performance optimization, and production deployment. • Experience with Git, CI/CD pipelines, automated testing, containerization, and cloud-based deployment environments using AWS, Google Cloud Platform (GCP), or Microsoft Azure. • Experience with AI orchestration frameworks such as LangChain, LlamaIndex, LangGraph, or similar tools. • Experience working with vector databases such as Pinecone, Weaviate, Qdrant, pgvector, or similar technologies. • Hands-on experience with cloud services and deployment on AWS, Google Cloud Platform (GCP), and Microsoft Azure. • Exposure to model evaluation, AI safety, guardrails, hallucination reduction, and observability for AI applications. • Experience building enterprise-grade SaaS products, internal platforms, automation tools, or customer-facing AI products. • Strong documentation, communication, problem-solving, and stakeholder management skills.