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Sr. AI Engineer (AI SDLC / Delivery)
Insight Global
2 hours ago
Full-time
On-site
Senior AI Engineer
Required Skills & Experience 10+ years of software engineering experience across design, development, deployment, and support 10+ years reviewing code and technical solutions against business and operational requirements 7+ years of backend development experience with C#/.NET (preferred) 5+ years of experience with React or similar frontend frameworks 5+ years of Terraform and Infrastructure as Code experience 5+ years designing and supporting CI/CD pipelines (Azure DevOps, GitHub Actions, etc.) 5+ years working with observability platforms including logging, monitoring, tracing, dashboards, alerting, and SLOs 5+ years of relational and NoSQL database experience 3+ years of Docker and Kubernetes experience Experience using AI coding tools and agentic workflows to accelerate software delivery Strong software architecture and distributed systems background Ability to evaluate, validate, and improve AI-generated code and infrastructure Strong cloud-native engineering experience Experience translating business requirements into technical solutions Strong testing experience across unit, integration, API, end-to-end, and infrastructure validation Nice to Have Skills & Experience Experience building and deploying agentic AI solutions in production LangGraph LangChain Azure AI Foundry Event-driven architecture experience Advanced Redis experience Legacy modernization experience AWS Certified Solutions Architect Professional AWS Certified DevOps Engineer Professional HashiCorp Terraform Associate Microsoft Azure Developer Associate Microsoft Azure DevOps Engineer Expert Certified Kubernetes Application Developer (CKAD) Job Description Job Overview Seeking a Senior AI Engineer to drive software delivery in an AI-assisted engineering environment. This individual will leverage AI coding agents and modern engineering practices to accelerate application development, testing, infrastructure deployment, and delivery while helping establish standards for AI-assisted software engineering across the organization. The initial project is a Tour Operations Portal supporting shore excursion inventory, vendor management, pricing, allocations, and operational workflows. This person will act as a technical lead, driving delivery while partnering across Product, Architecture, QE, and SRE teams. Day-to-Day Partner with business and technical teams to translate requirements into implementation plans Leverage AI coding agents to accelerate development, testing, and delivery Review AI-generated code, infrastructure, and deployment assets for quality, security, scalability, and maintainability Define guardrails, validation criteria, and best practices for AI-assisted delivery Guide AI-generated Terraform, CI/CD, and cloud infrastructure solutions Drive delivery for the Tour Operations Portal initiative and future enterprise application projects Identify technical debt, architectural risks, and implementation gaps Collaborate with Product, Architecture, QE, SRE, and business stakeholders Continuously improve AI engineering workflows, standards, and delivery processes
Required Skills & Experience 10+ years of software engineering experience across design, development, deployment, and support 10+ years reviewing code and technical solutions against business and operational requirements 7+ years of backend development experience with C#/.NET (preferred) 5+ years of experience with React or similar frontend frameworks 5+ years of Terraform and Infrastructure as Code experience 5+ years designing and supporting CI/CD pipelines (Azure DevOps, GitHub Actions, etc.) 5+ years working with observability platforms including logging, monitoring, tracing, dashboards, alerting, and SLOs 5+ years of relational and NoSQL database experience 3+ years of Docker and Kubernetes experience Experience using AI coding tools and agentic workflows to accelerate software delivery Strong software architecture and distributed systems background Ability to evaluate, validate, and improve AI-generated code and infrastructure Strong cloud-native engineering experience Experience translating business requirements into technical solutions Strong testing experience across unit, integration, API, end-to-end, and infrastructure validation Nice to Have Skills & Experience Experience building and deploying agentic AI solutions in production LangGraph LangChain Azure AI Foundry Event-driven architecture experience Advanced Redis experience Legacy modernization experience AWS Certified Solutions Architect Professional AWS Certified DevOps Engineer Professional HashiCorp Terraform Associate Microsoft Azure Developer Associate Microsoft Azure DevOps Engineer Expert Certified Kubernetes Application Developer (CKAD) Job Description Job Overview Seeking a Senior AI Engineer to drive software delivery in an AI-assisted engineering environment. This individual will leverage AI coding agents and modern engineering practices to accelerate application development, testing, infrastructure deployment, and delivery while helping establish standards for AI-assisted software engineering across the organization. The initial project is a Tour Operations Portal supporting shore excursion inventory, vendor management, pricing, allocations, and operational workflows. This person will act as a technical lead, driving delivery while partnering across Product, Architecture, QE, and SRE teams. Day-to-Day Partner with business and technical teams to translate requirements into implementation plans Leverage AI coding agents to accelerate development, testing, and delivery Review AI-generated code, infrastructure, and deployment assets for quality, security, scalability, and maintainability Define guardrails, validation criteria, and best practices for AI-assisted delivery Guide AI-generated Terraform, CI/CD, and cloud infrastructure solutions Drive delivery for the Tour Operations Portal initiative and future enterprise application projects Identify technical debt, architectural risks, and implementation gaps Collaborate with Product, Architecture, QE, SRE, and business stakeholders Continuously improve AI engineering workflows, standards, and delivery processes