Job Description A maritime client in Spring, TX is Seeking a Gen AI Full Stack Developer to join the Digital Prototyping Team. The team consists of 5 members, including data scientist, data engineer and 3 web and azure developers, this individual will work alongside the team, with independent responsibilities. The prototyping team meets with the business and takes their use cases/proof of concepts and enhances their software to meet those needs. Ideal candidate will excel in both web and mobile application development within the Azure cloud environment This is a hybrid role, requiring being onsite Tuesday-Thursday and will pay approximately $65/hr. We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements 5+ Years of Experience as a Full- Stack Developer Proficient in Generative AI, Azure, React, and JavaScript, Application Development & Integration: Develop and maintain production-ready applications and services using the Azure OpenAI Service APIs (e.g., Chat Completions, Embeddings, DALL-E) Prompt Engineering & Orchestration: Design, test, and refine effective prompts to achieve desired model outputs. Implement advanced prompt orchestration techniques using frameworks like LangChain or Semantic Kernel to build complex AI agents and workflows. Retrieval-Augmented Generation (RAG): Build and optimize RAG pipelines by integrating Azure OpenAI models with internal data sources Utilize services like Azure AI Search to ground models with proprietary information, ensuring accurate and contextually relevant responses Fine-Tuning: Fine-tune pre-trained models on custom datasets to improve performance, adapt to specific domains, or align with brand voice and tone Deployment & MLOps: Deploy and manage generative AI applications on Azure infrastructure Implement MLOps practices for monitoring, logging (e.g., Azure Monitor, Application Insights), and continuous improvement of AI solutions. Performance Optimization: Monitor and optimize API usage, latency, and cost of Azure OpenAI models Implement caching, batching, and other strategies for efficient operation.