Artificial Intelligence (AI) Engineer (Remote Position) - Knoxville, TN
Indexed description
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.
Responsibilities
- Create and launch AI and machine learning solutions that support product functionality, workflow automation, and data-driven decision-making across the platform.
- Build end-to-end ML workflows that cover data preparation, feature development, model training, validation, deployment, and ongoing performance oversight.
- Collaborate with product managers, software engineers, and data professionals to identify high-impact use cases for intelligent automation and advanced analytics.
- Develop generative AI applications such as content summarization, recommendation engines, classification tools, and agent-based experiences while balancing response speed, quality, and operating cost.
- Connect trained models to cloud-based production environments through APIs, service-oriented components, and containerized deployment patterns.
- Assess external AI platforms, libraries, and vendor solutions to determine their value for product enhancement and engineering productivity.
- Apply responsible AI practices by supporting model stability, bias awareness, data privacy, and security expectations throughout the development lifecycle.
- Maintain clear technical documentation, structured experiment records, and repeatable development processes that support transparency and collaboration.
- Track emerging trends in machine learning, large language models, and SaaS engineering to recommend improvements to tools, architecture, and delivery methods.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a closely related field, or equivalent practical experience.
- At least 3 years of experience designing, deploying, and supporting machine learning models in production environments.
- Strong Python development skills along with experience using machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Background working with cloud platforms such as AWS, Google Cloud, or Azure, plus container technologies including Docker and Kubernetes.
- Familiarity with large language models, vector databases, and contemporary AI tooling used for applied machine learning solutions.
- Understanding of API development and service-based architecture within modern software applications.
- Solid foundation in software engineering principles, including algorithms, data structures, code quality, and maintainable system design.
- Experience with model experimentation, evaluation practices, and monitoring approaches for production AI systems.
Create a free Caio profile to unlock more results and save your role and location preferences.
Unlock free search