M. Irfan Nazir
Platform Engineer - Actuarial @Manulife
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WORK HISTORY
Platform Engineer - Actuarial @Manulife
Toronto, ON, CA
Supported 10+ enterprise financial applications across 2,500+ servers within a regulated infrastructure environment. Designed and implemented end-to-end MLOps pipelines using Azure Machine Learning, MLflow, and Azure DevOps—automating model promotion from experimentation to production. Established LLMOps practices, building scalable infrastructure for deploying and serving large language models in production. Built and managed CI/CD pipelines for application and model deployments using Azure DevOps, GitHub Actions, AKS, Docker, and scripting frameworks. Led migration of 5+ on-prem applications to cloud-native architectures (Azure App Services, microservices, VMSS). Implemented automated data ingestion and migration pipelines using Azure Data Factory, Databricks, and ADLS to support analytics and ML workloads. Drove cloud cost optimization through automation and on-demand provisioning, contributing to ~$2M in infrastructure savings. Implemented Infrastructure as Code (Terraform) for reproducible, secure, and scalable cloud deployments. Designed disaster recovery, business continuity, and monitoring strategies using New Relic and Grafana. Enforced cloud security best practices including encryption, private endpoints, and secure network architecture.
EDUCATION
SZABIST Islamabad
Bachelor's degree, Computer Software Engineering
Athabasca University
Master of Science - MS, Computer Science
Hamdard University
Diploma, Computer Sciences
ABOUT M. IRFAN NAZIR
With 18+ years of experience in IT/Financial/Telecom industry including 8+ years of experience as Senior Cloud & DevOps Architect designing, implementing, and operating large-scale cloud infrastructure across Microsoft Azure and AWS. Deep expertise in infrastructure as code (Terraform/Bicep/CloudFormation), CI/CD pipeline engineering, containerization (Docker/Kubernetes), and cloud networking and security architecture. Strong hands-on experience in Machine Learning and MLOps, building scalable ML platforms, automating model training and deployment pipelines, and implementing reproducible, production-grade ML workflows. Experienced in designing end-to-end MLOps solutions that integrate data pipelines, model versioning, monitoring, governance, and continuous delivery within cloud-native environments. Recently working with generative and agentic AI solutions, integrating AI services into cloud-native architectures, building intelligent automation workflows, and enabling scalable AI-driven applications within enterprise environments Azure, AWS MLOps & LLMOps: ML MLflow, Azure Machine Learning, Model versioning and deployment, Vector Databases Prompt Engineering, Model Fine Tuning Terraform Ansible, Jenkins, Artifactory Azure DevOps, Jira, Confluence Git, GitHub GitHub Actions, GitHub Workflows, Dockers, Kubernetes, Helm, Helm Charts Python, PowerShell, Grafana, New Relic Windows Server, Hyper-V, VMware ESXi, Centos/Ubuntu
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