We are looking for an Artificial Intelligence (AI) Engineer to join a long-term contract opportunity based in Cincinnati, Ohio. In this role, you will create and operationalize AI and machine learning solutions with a strong focus on generative AI, helping translate business needs into secure, scalable applications. You will collaborate across technical and business teams to deliver production-ready models, strengthen AI adoption, and support measurable outcomes through thoughtful engineering and evaluation.<br><br>Responsibilities:<br>• Create, train, and deploy scalable AI and machine learning solutions, including generative AI applications such as large language models, retrieval-augmented systems, and prompt-driven workflows.<br>• Build and maintain data pipelines, feature preparation processes, and model training and evaluation jobs using Python and established machine learning frameworks.<br>• Deliver models into production as APIs or batch processes, and improve inference performance with attention to speed, reliability, and cost efficiency.<br>• Assess emerging generative AI tools and model architectures, including embeddings, vector search, and diffusion-based approaches, to support defined business use cases.<br>• Develop prompt strategies, orchestration flows, safety controls, and evaluation methods, using structured testing to improve response quality and reduce risk.<br>• Apply MLOps and LLMOps practices such as experiment tracking, version control, deployment automation, monitoring, alerting, and drift detection.<br>• Integrate AI services with enterprise data platforms and business systems, including APIs, event-driven architectures, data warehouses, and reporting environments.<br>• Partner with product, analytics, IT, and data architecture teams to clarify requirements, document technical decisions, and deliver solutions that meet agreed objectives.<br>• Promote responsible AI practices by incorporating privacy, security, fairness, and content safety considerations into model behavior, testing, and deployment.<br>• Track performance indicators for AI initiatives, share insights on effectiveness and adoption, and stay current with advances in generative AI and broader machine learning methods.

• Hands-on experience designing and deploying AI or machine learning solutions in production environments.<br>• Strong proficiency in Python and common ML libraries such as TensorFlow, along with experience building training and inference workflows.<br>• Practical knowledge of generative AI concepts, including large language models, prompt engineering, retrieval-augmented generation, embeddings, or vector search.<br>• Understanding of MLOps or LLMOps practices, including model versioning, CI/CD, monitoring, and performance validation.<br>• Experience integrating AI applications with data platforms, APIs, and enterprise systems in a secure and reliable manner.<br>• Familiarity with responsible AI principles covering privacy, security, fairness, and model safety.<br>• Ability to work effectively with cross-functional stakeholders and communicate technical trade-offs, assumptions, and progress clearly.<br>• Background in machine learning domains such as computer vision, detection, or related AI disciplines is preferred.

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