Artificial Intelligence (AI) Engineer

Description
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. Requirements
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.

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