MLOps and AI Infrastructure Engineer with 3+ years of experience designing secure, scalable ML pipelines and cloud-native model deployment systems used in high-reliability environments, including financial-grade applicat…
MLOps and AI Infrastructure Engineer with 3+ years of experience designing secure, scalable ML pipelines and cloud-native model deployment systems used in high-reliability environments, including financial-grade applications.
Built automated MLOps pipelines with MLflow, CI/CD, and model versioning, enabling audit-ready and compliant deployments for fintech workloads.
Designed secure ML environments with IAM, encryption, and CloudTrail to meet BFSI-style governance.
Provisioned AWS ML infrastructure (EKS, S3, registries, endpoints) using Terraform.
Hardened clusters with RBAC, PodSecurity, Trivy, and Kube-bench.
Containerized and scaled inference via Docker + EKS HPA, boosting reliability by 50%.
Deployed FastAPI/MLflow model servers.
Built Prometheus–Grafana dashboards for latency, drift, and API health.
Reduced model deployment overhead by 60% through reusable templates and automation.
Built automated MLOps pipelines with MLflow, CI/CD, and model versioning for fintech workloads, Provisioned AWS ML infrastructure (EKS, S3, registries, endpoints) using Terraform, Deployed FastAPI/MLflow model servers and built Prometheus-Grafana dashboards
Developed a modular Kubeflow pipeline with preprocessing, training, evaluation, and auto-retraining. • Demonstrated strong competence in orchestrated ML workflows at scale. • Deployed models via KFServing, for strong orchestration and pipeline automation for production ML systems.
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