Join a dynamic team as a Lead DevOps Engineer, spearheading the design and operation of essential engineering practices for building, releasing, and running scalable digital solutions, including AI-powered products. This role demands a blend of infrastructure automation expertise and a strong full-stack engineering perspective, enabling you to understand and optimize complex production systems from frontend to backend, APIs, data services, and AI components.

You'll collaborate closely with cross-functional engineering teams, guiding CI/CD strategies, environment standardization, release automation, and overall platform reliability. The ideal candidate possesses extensive experience in DevOps, cloud platforms, automation, containerization, and observability, complemented by a solid grasp of application architecture and production support expectations.

Lead the development and enhancement of CI/CD pipelines, deployment workflows, and release automation for cutting-edge digital and AI products. Build and manage cloud-native infrastructure and runtime platforms supporting diverse applications, APIs, and AI workloads. Partner with engineering teams to elevate deployability, testability, scalability, observability, and resilience across the entire product stack. Design and maintain infrastructure-as-code, manage environments, secure credentials, and ensure consistent deployments. Support containerized services, microservices, and AI components, contributing architectural insights from a full-stack production viewpoint. Implement comprehensive observability solutions, including logs, traces, metrics, and alerts. Enable AI/ML workloads through optimized deployment environments and monitoring. Drive operational readiness via runbooks, incident response, and continuous improvement. Standardize engineering practices and mentor engineers on best practices, fostering reusable accelerators.

Requires 6-8+ years of experience in DevOps, platform, SRE, cloud, or software engineering with proven experience operating production systems. Demonstrated expertise in building CI/CD pipelines, cloud-native deployments, containerization, and infrastructure automation is essential. Strong hands-on proficiency with tools like Azure DevOps, GitHub Actions, Terraform, Docker, and Kubernetes is expected. A deep understanding of cloud-native application delivery, including backend APIs, event-driven services, and full-stack deployment patterns, is critical. Practical experience with modern web architectures and troubleshooting across the stack is necessary. Extensive experience with observability tools such as Azure Monitor, Prometheus, or Grafana is required. Familiarity with AI/ML workloads and their deployment/monitoring needs is a plus. Excellent communication and technical leadership skills, including mentoring, are vital.