AI Engineer

Required Skills & Experience

5+ years of full-time experience in electronic data processing systems study, design, and programming.\nBachelor’s degree in an IT-related or Engineering field; additional qualifying experience may be substituted on a year-for-year basis.\n4+ years of experience designing, developing, and implementing applications in C# and containerized environments such as Docker and Kubernetes.\n2+ years of experience working with AI/ML models, including fine-tuning and integrating models using AWS Bedrock and Azure AI services.\n3+ years of experience working with RESTful APIs and integrating external services into enterprise applications.\n3+ years of experience building CI/CD pipelines for AI models using GitHub Actions and Terraform.

Nice to Have Skills & Experience

3+ years of experience with AI frameworks such as LangChain, MLflow, Hugging Face, or TensorFlow, plus 2+ years of Infrastructure as Code experience with Terraform or AWS CloudFormation.\n2+ years of experience developing AI-powered chatbots, decision-support systems, or automation agents, along with implementing security best practices for cloud-based AI services.\n2+ years of experience optimizing AI models for performance and cost efficiency in AWS and Azure; prior experience supporting healthcare or government-related AI applications is preferred.

Job Description

Insight Global is seeking a Senior AI Engineer to join a critical project with one of the largest consulting and advisory firms in the world.\nThis role will support a cloud-native AI engineering initiative for the State of California.\nThe Senior AI Engineer will help design, build, and deploy AI agents and workflow-driven applications that integrate with enterprise platforms and external services.\nThis person should bring strong hands-on experience across C#, containerized environments, AI/ML services, REST APIs, and modern CI/CD practices to deliver scalable, secure, and production-ready solutions.\n\nDesign, develop, and implement cloud-native applications in C# to support AI-driven workflows and agent-based solutions.\nBuild and deploy services within containerized environments such as Docker and Kubernetes.\nDevelop and integrate AI and ML capabilities using AWS Bedrock and Azure AI services, including model fine-tuning and enterprise integration.\nCreate and maintain RESTful APIs and connect external services into broader enterprise application architectures.\nSupport the development of AI model deployment pipelines using GitHub Actions and Terraform.\nPartner with technical teams to deliver secure, scalable, and efficient AI solutions within AWS and Azure environments.\nContribute to the design of automation agents, decision-support capabilities, and chatbot-style experiences where applicable.\nApply engineering best practices to optimize application and model performance, reliability, and cost efficiency.\nHelp establish reusable development patterns and workflows for AI solutions operating within a cloud-native platform environment.