Lead AI Engineer in Cincinnati

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Job Description

We are looking for an AI Lead Engineer who is responsible for designing, implementing, and maintaining intelligent systems and infrastructure that support enterprise-scale AI solutions. This role leads the development of AI platforms, automation frameworks, deployment pipelines, and scalable cloud-based architectures. The AI Lead Engineer partners closely with engineering, product, and data teams to deliver secure, reliable, and high‑performance AI services across the organization.

Responsibilities:

• Architect and implement AI infrastructure services including model hosting, inference pipelines, feature stores, vector databases, and scalable compute environments.
• Develop the strategic roadmap for AI platforms, automation, and cloud services, including management, data access, model lifecycle governance, patching, and security posture.
• Lead the engineering, deployment, and optimization of cloud‑ AI systems across AWS and other cloud providers.
• Design, implement, and support MLOps and DevOps pipelines enabling continuous integration, continuous delivery, and continuous training (CI/CD/CT).
• Analyze business and technical requirements to propose scalable, secure AI solutions aligned with enterprise standards.
• Ensure end‑to‑end reliability and performance of AI systems, including monitoring, observability, backup and restoration, and infrastructure capacity planning.
• Integrate AI applications and services into existing enterprise systems, ensuring adherence to security, compliance, and standards.
• Troubleshoot complex distributed systems across compute, storage, networking, and data layers to resolve issues impacting AI workloads.
• Stay current with emerging AI and cloud technologies, evaluating tools, frameworks, and architectures that elevate the organization’s capabilities.

The Ideal Candidate:

• Experience with one or more major cloud platforms; AWS required.
• Expert‑level knowledge of Linux and Windows server operating systems and enterprise‑scale cloud engineering.
• Strong experience with AI/ML infrastructure, MLOps practices, or automation platforms.
• Background in containerization and orchestration (Docker, Kubernetes, ECS, EKS).
• Experience with automation and configuration management tools.
• Deep knowledge of CI/CD pipelines, automated deployment processes, and infrastructure as code.
• Proficiency with scripting and programming such as Python, PowerShell, PHP, Ruby, or Perl.
• Hands‑on experience with monitoring, observability, and performance tuning for cloud-based services.
• Understanding of version control systems, particularly Git, and modern engineering workflows.
• Experience working in Agile environments.
• Familiarity with and directory services (Active Directory, SSO, IAM).
• Experience with virtualization, distributed systems, and enterprise infrastructure services.

If you are interested in applying for this job please press the Apply Button and follow the application process. Energy Jobline wishes you the very best of luck in your next career move.