AI Development Fullstack Developer

Role Overview

As an AI Development Fullstack Developer, you will be responsible for creating and integrating enterprise-grade AI solutions. Your projects will encompass generative AI applications, retrieval-augmented generation (RAG) pipelines, AI agents, copilots, automation workflows, and reusable AI platform features.

Key Responsibilities

• Design and develop GenAI applications, RAG components, prompt flow mechanisms, data connectors, APIs, and AI agents.
• Implement responsive user interfaces employing React, Next.js, TypeScript, HTML5, CSS3, and modern UI libraries such as Material UI or Tailwind CSS.
• Leverage Azure AI services to build vector indexes, manage embeddings, process documents, and perform knowledge grounding.
• Create proofs of concept (PoCs), minimum viable products (MVPs), and reusable accelerators to support client demonstrations and accelerate delivery.
• Participate in testing, including evaluation, groundedness verification, performance tuning, continuous monitoring, and cost optimization initiatives.
• Maintain thorough documentation covering designs, deployment procedures, and reusable implementation patterns.
• Collaborate effectively with consultants, product owners, solution architects, UX designers, and governance teams.

Required Experience and Skills

• Between five to eight years working in software development, data engineering, AI, cloud development, or automation fields.
• Practical experience creating generative AI systems, Agentic Harness frameworks, retrieval-augmented generation, chatbots, AI/ML models, or enterprise integration solutions.
• Proficient in Python programming with complementary frontend development skills and familiarity with APIs, software development kits (SDKs), and version control systems.
• Experience contributing to agile software teams and the capacity to convey technical trade-offs clearly.
• Knowledge of backend frameworks like FastAPI or similar, along with modern frontend technologies using React or Next.js.
• Familiarity with Azure AI ecosystem, including Azure OpenAI, AI Foundry, AI Search, and Azure Machine Learning basics.
• Expertise in RAG pipeline design, embedding techniques, vector search methods, prompt engineering, and evaluation strategies.
• Hands-on experience with agent frameworks, orchestration strategies, tool invocation, and workflow automation.
• Background integrating APIs, various databases, message queues, file systems, and enterprise connectors.
• Competence in CI/CD pipelines, Git version control, containerization technologies, configuration management, and automated testing.
• Strong focus on AI quality assurance including test datasets, regression testing, hallucination detection, groundedness validation, latency monitoring, and cost control.
• Implementation of responsible AI approaches such as content filtering, protective guardrails, logging mechanisms, and human-in-the-loop review.