Full Stack Developer – AI

Join a technology-driven team focused on building, maintaining, and optimizing production-ready Generative AI applications. In this role, you will contribute to the full software development lifecycle, from enhancing frontend experiences and backend services to integrating advanced AI capabilities. You will work hands-on with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) pipelines, and prompt engineering to deliver reliable, scalable AI-powered solutions. Your expertise will help troubleshoot complex production issues, improve application performance, and strengthen platform stability. Collaborating with engineering, support, and cloud infrastructure teams, you will drive continuous improvement and operational excellence. This 12-month contract offers an opportunity to work with modern AI technologies and cloud-native development practices in a dynamic engineering environment.

• Full Stack Development: Strong experience in full stack software development, including frontend and backend application engineering.

• Frontend Technologies: Practical experience with React, Angular, and Node.js for building and maintaining modern applications and services.

• Generative AI Expertise: Hands-on experience with LLMs, RAG pipelines, and prompt engineering, along with an understanding of GenAI application architecture.

• AI Lifecycle Knowledge: Familiarity with AI model lifecycle management, integration patterns, and the challenges associated with deploying AI-powered applications in production.

• Cloud Platforms: Experience working with Microsoft Azure and/or AWS cloud environments.

• DevOps and Containerization: Knowledge of CI/CD pipelines, Docker, Kubernetes, and modern software deployment practices.

• Database Technologies: Strong understanding of SQL and NoSQL databases, with familiarity with vector databases and data ingestion frameworks.

• Production Operations: Experience supporting production applications, troubleshooting complex technical issues, conducting root cause analysis, and implementing sustainable fixes.

• AI Quality and Reliability: Awareness of AI-specific risks, including hallucinations, model drift, bias, and reliability challenges.

• Analytical Skills: Strong problem-solving, debugging, and performance optimization capabilities, with a structured approach to technical challenges.

• Communication and Collaboration: Excellent communication skills and the ability to work effectively with cross-functional teams in a fast-paced technical environment.

• Work Approach: A proactive, quality-focused mindset, with the ability to balance application development, operational support, and continuous improvement.