Rohith Challa

DevOps Engineer

Pune, Maharashtra, India0 mo experience

Key Highlights

Expert in CI/CD pipeline implementation and automation.
Hands-on experience with cloud platforms and containerization.
Strong foundation in MLOps and data engineering practices.

Stackforce AI infers this person is a DevOps and MLOps engineer with expertise in cloud infrastructure and automation.

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Skills

Core Skills

DevopsCi/cd Pipeline ImplementationCloud Deployment & AutomationMlopsData Engineering

Other Skills

ANALYSIS & PRESENTATIONAWSAd Hoc AnalysisAmazon Web Services (AWS)AnsibleAzure DatabricksAzure DevOpsBusiness Intelligence (BI)CI/CDDAXDashboardsData AnalyticsData VisualizationDockerExtract, Transform, Load (ETL)

Experience

Wipro

2 roles

DevOps Engineer

Sep 2025 – Present · 6 mos

DevOps Engineer Trinee

Jul 2025 – Sep 2025 · 2 mos

Completed a 45-day intensive DevOps training program covering core tools and practices including Docker, Kubernetes, Ansible, Git/GitHub, Jenkins, SonarQube, and CI/CD pipelines.
Successfully deployed multiple hands-on projects such as:
3-Tier Application Deployment
Ingress Application Deployment
Movie Booking Application (BookMyShow Clone on AWS EKS)
Gained practical experience in containerization, orchestration, infrastructure automation, and code quality analysis.
Demonstrated strong problem-solving skills, consistency, and the ability to quickly apply theoretical knowledge to real-world scenarios.

DockerKubernetesAnsibleGitGitHubJenkins+4

Apexon

MLOps and Data Intern

Mar 2025 – Jul 2025 · 4 mos · Hyderabad, Telangana, India · Hybrid

During my internship at Apexon, I worked on a full-scale MLOps project focused on hospital mortality prediction using the MIMIC-III dataset. I was responsible for designing, developing, and deploying a modular and scalable pipeline using Azure Databricks and Spark MLlib to support real-time and batch processing of synthetic healthcare data.
I implemented synthetic data generation strategies to simulate patient records for privacy-preserving model development, followed by rigorous data preprocessing including null value imputation, outlier handling, scaling, and feature assembly using PySpark. My work included extensive EDA with histograms, box plots, and KPI analysis to identify data patterns and quality issues.
For model training, I applied logistic regression using Spark’s MLlib framework and evaluated model performance using ROC-AUC metrics. I also visualized ROC curves and managed model artifacts in a structured format for downstream use.
A key highlight of the internship was automating the pipeline using Azure DevOps CI/CD. I created YAML-based workflows to run training and inference jobs in a reproducible and automated manner. This significantly improved the reliability and scalability of model deployments.
Additionally, I created reusable Python modules for model training, evaluation, comparison, and inference — enabling future integration with tools like MLflow, Docker, and API frameworks. The final pipeline supports both real-time scoring and batch inference, making it production-ready and cloud-native.
This experience strengthened my understanding of MLOps practices, cloud engineering, and collaborative ML workflows in a healthcare domain. It also gave me hands-on exposure to enterprise-level tools like Databricks, Spark, and Azure DevOps, preparing me for real-world machine learning engineering roles.

Azure DatabricksSpark MLlibPySparkAzure DevOpsLogistic RegressionMLOps+1