Sfoglia categorie
Esplora
Fiverr Pro
Italiano
$
USD
Ravikanth Dulam
@ravikanthdulam
DevOps and MLOps pipelines using Azure DevOps, Aws
India
Inglese, Telugu, Hindi
Chi sono
Senior DevOps & MLOps Engineer with 6+ years of experience in AWS, Azure, Kubernetes, Docker, Terraform and CI/CD. I specialize in cloud infrastructure, Kubernetes deployments, automation, monitoring and production MLOps. I work with Jenkins, Azure DevOps, GitHub Actions, MLflow, Prometheus and Grafana to build secure, scalable and reliable solutions.
Competenze
Ravikanth Dulam
offline •
Consulta i miei servizi
Consulenza DevOps
I will build mlops and llm pipelines on AWS azure using kubernetes
Containerizzazione DevOps
I will build azure mlops pipeline with mlflow model registry and drift detection
Portfolio
Esperienza lavorativa
Senior DevOps & MLOps Engineer
Upwork • Full time
Dec 2019 - Present • 6 yrs 9 mos
Leading DevOps & MLOps engineering across AWS and Azure for enterprise clients. Architected end-to-end CI/CD pipelines using Azure DevOps, GitHub Actions, and Jenkins — reducing deployment time by 50% and release failures by 35%. Built production-grade MLOps platform on Azure Machine Learning with automated training pipelines, MLflow experiment tracking, model registry, and drift detection — cutting data science release cycles from weeks to days. Deployed LLM inference pipelines using LangChain, vLLM, and RAG architecture with Pinecone vectorstores on Kubernetes AKS achieving sub-200ms latency at 500+ RPS. Managed AKS clusters supporting 50+ microservices maintaining 99.9% production uptime. Embedded DevSecOps toolchain reducing critical CVEs by 40%. Reduced cloud costs by 25% through rightsizing and Reserved Instance management.
Senior DevOps & MLOps Engineer
Multiscale AI • Full time
Dec 2019 - Present • 6 yrs 9 mos
Leading DevOps & MLOps engineering across AWS, Azure and GCP for enterprise clients. Architected end-to-end CI/CD pipelines using Azure DevOps, GitHub Actions and Jenkins — reducing deployment time by 50% and release failures by 35%. Built production-grade MLOps platform on Azure Machine Learning with automated training pipelines, MLflow experiment tracking, model registry and drift detection — cutting data science release cycles from weeks to days. Deployed LLM inference pipelines using LangChain, vLLM and RAG on Kubernetes (AKS) achieving sub-200ms latency at 500+ RPS. Managed AKS clusters supporting 50+ microservices with 99.9% uptime. Integrated HashiCorp Vault + Azure Key Vault for automated secret rotation and least-privilege access. Embedded DevSecOps toolchain reducing critical CVEs by 40%. Reduced cloud costs by 25% through rightsizing and Reserved Instances.
DevOps & MLOps Engineer
Cyient • Full time
Nov 2019 - Present • 6 yrs 10 mos
Delivered CI/CD automation, container orchestration and MLOps workflows across AWS and Azure supporting data science and engineering teams. Built Jenkins (shared libraries) and Azure DevOps pipelines for 5+ applications — improved average build time by 30%. Containerized ML workloads with multi-stage Dockerfiles (40% smaller images) and deployed on AKS/ECS. Designed model lifecycle management (registration → staging → production, approval gates, rollback). Authored Terraform + Ansible automation for EC2, S3, VPC and IAM — reduced manual provisioning by 60%. Configured NGINX with TLS termination and integrated Azure Key Vault for secure secret injection. Collaborated with data science teams on ML training pipeline debugging and S3 dataset versioning.
