Artificial Intelligence (AI) Engineer

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.

Responsibilities:

• 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.
• Build and maintain data pipelines, feature preparation processes, and model training and evaluation jobs using Python and established machine learning frameworks.
• Deliver models into production as APIs or batch processes, and improve inference performance with attention to speed, reliability, and cost efficiency.
• Assess emerging generative AI tools and model architectures, including embeddings, vector search, and diffusion-based approaches, to support defined business use cases.
• Develop prompt strategies, orchestration flows, safety controls, and evaluation methods, using structured testing to improve response quality and reduce risk.
• Apply MLOps and LLMOps practices such as experiment tracking, version control, deployment automation, monitoring, alerting, and drift detection.
• Integrate AI services with enterprise data platforms and business systems, including APIs, event-driven architectures, data warehouses, and reporting environments.
• Partner with product, analytics, IT, and data architecture teams to clarify requirements, document technical decisions, and deliver solutions that meet agreed objectives.
• Promote responsible AI practices by incorporating privacy, security, fairness, and content safety considerations into model behavior, testing, and deployment.
• 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.
• Strong proficiency in Python and common ML libraries such as TensorFlow, along with experience building training and inference workflows.
• Practical knowledge of generative AI concepts, including large language models, prompt engineering, retrieval-augmented generation, embeddings, or vector search.
• Understanding of MLOps or LLMOps practices, including model versioning, CI/CD, monitoring, and performance validation.
• Experience integrating AI applications with data platforms, APIs, and enterprise systems in a secure and reliable manner.
• Familiarity with responsible AI principles covering privacy, security, fairness, and model safety.
• Ability to work effectively with cross-functional stakeholders and communicate technical trade-offs, assumptions, and progress clearly.
• Background in machine learning domains such as computer vision, detection, or related AI disciplines is preferred.