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Results-driven DevOps/Site Reliability Engineer focused on building and operating reliable cloud and AI/MLOps platforms. Experienced across AWS, GCP, and Azure, with strong hands-on expertise in Kubernetes, Docker, CI/CD, and Infrastructure as Code. Supports production AI/ML workloads with model deployment, monitoring/observability, automation, and SLI/SLO-driven operations. Known for reducing operational toil, improving deployment reliability and speed, and optimizing performance, latency, scalability, and cost through practical engineering and incident-driven improvements.…Results-driven DevOps/Site Reliability Engineer focused on building and operating reliable cloud and AI/MLOps platforms. Experienced across AWS, GCP, and Azure, with strong hands-on expertise in Kubernetes, Docker, CI/CD, and Infrastructure as Code. Supports production AI/ML workloads with model deployment, monitoring/observability, automation, and SLI/SLO-driven operations. Known for reducing operational toil, improving deployment reliability and speed, and optimizing performance, latency, scalability, and cost through practical engineering and incident-driven improvements.

Babajide Fatile

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Results-driven DevOps/Site Reliability Engineer focused on building and operating reliable cloud and AI/MLOps platforms. Experienced across AWS, GCP, and Azure, with strong hands-on expertise in Kubernetes, Docker, CI/CD, and Infrastructure as Code. Supports production AI/ML workloads with model deployment, monitoring/observability, automation, and SLI/SLO-driven operations. Known for reducing operational toil, improving deployment reliability and speed, and optimizing performance, latency, scalability, and cost through practical engineering and incident-driven improvements.…Results-driven DevOps/Site Reliability Engineer focused on building and operating reliable cloud and AI/MLOps platforms. Experienced across AWS, GCP, and Azure, with strong hands-on expertise in Kubernetes, Docker, CI/CD, and Infrastructure as Code. Supports production AI/ML workloads with model deployment, monitoring/observability, automation, and SLI/SLO-driven operations. Known for reducing operational toil, improving deployment reliability and speed, and optimizing performance, latency, scalability, and cost through practical engineering and incident-driven improvements.

Available to hire

Results-driven DevOps/Site Reliability Engineer focused on building and operating reliable cloud and AI/MLOps platforms. Experienced across AWS, GCP, and Azure, with strong hands-on expertise in Kubernetes, Docker, CI/CD, and Infrastructure as Code.

Supports production AI/ML workloads with model deployment, monitoring/observability, automation, and SLI/SLO-driven operations. Known for reducing operational toil, improving deployment reliability and speed, and optimizing performance, latency, scalability, and cost through practical engineering and incident-driven improvements.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Work Experience

Site Reliability Engineer / DevOps Engineer at ServiceTitan Software

February 1, 2022 - Present

Engineered highly available cloud infrastructure and automation frameworks across AWS and Azure, reducing operational toil by 58% while improving platform reliability and deployment efficiency. Established observability frameworks and SLO-driven operations that reduced MTTR and minimized recurring incidents. Operated production distributed workloads on AWS EKS/Azure AKS using Kubernetes and Docker, with Terraform/Ansible for immutable infrastructure provisioning. Built Python- and Bash-based automation and AI-enhanced operational tooling for anomaly detection, diagnostics, and remediation. Implemented GitOps delivery, optimized Kubernetes autoscaling (HPA/VPA/KEDA), and investigated complex performance/latency issues across compute, networking, storage, and applications. Developed observability using Prometheus, Grafana, ELK, OpenTelemetry, and Datadog (metrics/logs/traces) including alerting and workflows applicable to ML services. Applied SLI/SLO frameworks, incident response, post

Cloud Engineer at Mavencode Software

January 1, 2020 - March 1, 2022

Architected and deployed cloud-native platforms across AWS and Azure achieving 99.99% availability while reducing cloud costs by 35%. Improved deployment speed by 65% and reduced manual operational effort through infrastructure automation and CI/CD. Designed scalable Kubernetes platforms on AWS EKS and Azure AKS, implementing HPA/VPA/KEDA autoscaling to maintain availability. Built CI/CD pipelines for containerized apps with automated testing, security scanning, artifact management, and deployment automation (release cycle time reduced by 65%). Supported cloud/ML concepts including cloud ML services, containerized workloads, model-serving infrastructure, and production observability. Implemented cloud security (IAM, Zero Trust, least-privilege, secrets management) and automated scaling, disaster recovery, and operational workflows using Terraform/Ansible with Python/Bash. Troubleshot production failures across compute, networking, databases, containers, and distributed services withi