Zaheer Hussain

About Zaheer Hussain

DevOps/MLOps Engineer with 3.5+ years of experience building secure, scalable, and automated cloud infrastructure across Azure, AWS, and GCP. Skilled in CI/CD pipeline design (GitLab CI/CD, Jenkins, Azure DevOps, GitHub Actions, ArgoCD), container orchestration (Docker, Kubernetes, Helm), and Infrastructure as Code (Terraform, Ansible, Bicep). Alongside DevOps, I have developed strong specialization in AI/MLOps, integrating ML workflows into CI/CD pipelines and cloud‑native environments to enable intelligent automation and scalable ML deployments. With proven success in cloud migration, GitOps workflows, and DevSecOps integration, I deliver automation, AI‑driven workflows, and resilient microservice deployments that accelerate delivery and reduce costs.I have architected and automated enterprise workloads, optimized CI/CD pipelines to cut deployment times by 30%, and reduced post‑deployment issues by 70%. I implemented secure credential rotation (HashiCorp Vault, Azure Key Vault, AWS Secrets Manager), integrated SonarQube, Veracode, and Semgrep for proactive vulnerability detection, and leveraged Datadog, Grafana, Prometheus, and CloudWatch to enhance observability and efficiency. My GitOps implementations with FluxCD and ArgoCD eliminated manual deployments and improved consistency by 60%, while containerization strategies reduced infrastructure costs by 20% through autoscaling and resource optimization.In parallel, I have built expertise in AI/MLOps, automating ML pipelines, model training, deployment, and monitoring using MLflow, Kubeflow, and AWS SageMaker. I have implemented reproducible ML workflows across 100+ runs, trained models with Random Forest, Gradient Boosting, and Neural Networks, and incorporated feature engineering, drift detection, and secure deployments. My work includes model versioning, experiment tracking, and managing model registries to streamline collaboration between data scientists and engineers. By aligning DevOps automation with MLOps workflows, I accelerated AI delivery by 30% and enabled production‑ready ML solutions at scale.Certified in Azure Fundamentals, Azure AI Fundamentals, AWS Cloud Practitioner, and Google Cloud Digital Leader, I am committed to continuous learning and advancing enterprise DevOps and AI/MLOps practices. I now seek opportunities to contribute to scalable cloud automation, AI‑powered DevOps innovation, and secure CI/CD transformation, collaborating with teams that value automation, innovation, and reliability.

Experience

1. Technology Analyst

   Infosys

   Jan 2026 — Present · Bengaluru, IN

   AI/MLOps Pipelines: Designed and automated end‑to‑end ML lifecycle workflows (data ingestion, preparation, training, evaluation, deployment, monitoring) using MLflow, Kubeflow, and SageMaker; ensured reproducibility across 100+ runs.Cloud Infrastructure Automation: Architected multi‑cloud provisioning with Terraform and GitLab CI/CD runners, enabling zero‑touch deployments on Azure & AWS with dynamic variables, remote state, and policy‑as‑code.Kubernetes & Microservices: Engineered AKS/EKS clusters with multi‑node pools, autoscaling, and RBAC; achieved 99.99% uptime. Directed Helm‑based blue‑green/canary rollouts for seamless microservice delivery.CI/CD Optimization: Streamlined pipelines (GitLab, Docker.NET CLI) with caching, parallel jobs, and rollback automation; reduced deployment time by 30% and improved reliability.Security & Compliance: Automated secret/cert rotation via Azure Key Vault and HashiCorp Vault; enforced least‑privilege policies and achieved SOC2/ISO27001 compliance.Governance & Drift Control: Embedded PowerShell/Bash validation scripts to detect drift and policy violations; reduced post‑deployment issues by 70%.Cloud‑Native Transformation: Led migration of legacy monoliths to containerized stacks (Docker, Kubernetes, Istio), improving uptime with self‑healing workloads and autoscaling.AI‑Driven DevOps: Integrated MLflow/Kubeflow into CI/CD workflows; predictive models forecasted failures and auto‑remediation cut incident resolution by 60%.Observability & Reliability Engineering: Deployed Datadog, Prometheus, Grafana for anomaly detection and proactive alerting; improved MTTR and system reliability.Cross‑Functional Collaboration: Partnered with dev, security, and data teams to resolve infra bottlenecks, optimize costs, and deliver production‑grade, SLA‑aligned environments.

Education

• Queen Mary's Senior Secondary School

  10th, General

• New Wisdom Public School

  12th , Physics Chemistry And Maths

• Sagar Institute of Research Technology & Science (SIRTS), Near ISRO, Ayodhya Nagar, By Pass Road, Bhopal - 462041

  Bachelor of Technology - BTech, Computer Science

  2018 — 2022