Add your latest resume to match with open positions.
1 result for Artificial Intelligence Ai Engineer in Knoxville, TN
- Knoxville, TN
- remote
- Permanent / Full Time
- 0 - 0 USD / Yearly
- <p>Robert Half is hiring! We are looking for an Artificial Intelligence (AI) Engineer to create and deliver intelligent capabilities that strengthen a SaaS product within the finance consulting space. This position partners with product, data, and engineering teams to turn business needs into practical AI and machine learning solutions. The ideal candidate brings strong hands-on experience with production ML systems, a thoughtful approach to scalable architecture, and a focus on building features that improve user outcomes and operational efficiency.</p><p><br></p><p><strong>The best candidate for this role is someone that is still a hands on coder. We are looking for back end software engineers that also have skills and a passion for AI. </strong></p><p><br></p><p>Responsibilities:</p><p>• Create and launch AI and machine learning solutions that support product functionality, workflow automation, and data-driven decision-making across the platform.</p><p>• Build end-to-end ML workflows that cover data preparation, feature development, model training, validation, deployment, and ongoing performance oversight.</p><p>• Collaborate with product managers, software engineers, and data professionals to identify high-impact use cases for intelligent automation and advanced analytics.</p><p>• Develop generative AI applications such as content summarization, recommendation engines, classification tools, and agent-based experiences while balancing response speed, quality, and operating cost.</p><p>• Connect trained models to cloud-based production environments through APIs, service-oriented components, and containerized deployment patterns.</p><p>• Assess external AI platforms, libraries, and vendor solutions to determine their value for product enhancement and engineering productivity.</p><p>• Apply responsible AI practices by supporting model stability, bias awareness, data privacy, and security expectations throughout the development lifecycle.</p><p>• Maintain clear technical documentation, structured experiment records, and repeatable development processes that support transparency and collaboration.</p><p>• Track emerging trends in machine learning, large language models, and SaaS engineering to recommend improvements to tools, architecture, and delivery methods.</p>
- 2026-06-10T00:00:00Z