A Generative Artificial Intelligence Engineer develops, designs, and manages generative models and algorithms that support the generation of new content in the form of images, text, audio, and other multimedia. They utilize GPTs, GANs, VAEs, and other deep learning architectures to craft systems capable of generating data. May work with data scientists, machine learning engineers, and software developers.
We are seeking an experienced AI Architect to design, build, and deploy enterprise-scale AI and LLM solutions. This role requires a hands-on technical leader who can architect end-to-end AI/ML platforms, develop production-grade applications, and implement advanced agentic workflows using AWS Agent Core. The ideal candidate will combine deep expertise in AI engineering, cloud infrastructure, and software development with the ability to mentor teams and establish best practices for scalable AI delivery. Key Responsibilities Design and implement end-to-end AI/ML architectures and develop production-ready solutions using modern AI frameworks and cloud-native technologies. Architect, provision, and manage secure, scalable, and resilient AI infrastructure on AWS using Terraform and Infrastructure as Code principles. Build, optimize, and deploy advanced multi-agent systems and orchestration frameworks leveraging AWS Agent Core, Amazon Bedrock Agents, and Knowledge Bases. Develop and maintain robust data, model, and inference pipelines that integrate LLMs, vector databases, and enterprise applications. Implement Retrieval-Augmented Generation (RAG) solutions and optimize prompt engineering strategies for enterprise AI use cases. Establish and drive best practices across AI Engineering, MLOps, and LLMOps to ensure scalable, reliable, and governed AI deployments. Provide technical leadership, mentor engineering teams, and collaborate with business and technology stakeholders to deliver AI initiatives successfully. Required Skills & Qualifications Strong experience designing and deploying AI/ML and Generative AI solutions from proof of concept through production implementation.
