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Lead DevOps (Cloud Operations) with 6+ years in Cloud Infrastructure, Automation & CI/CD
Seeking a DevOps Engineer role to apply 6+ years of hands-on experience in cloud infrastructure, automation, and CI/CD pipelines. DevOps Engineer with 6+ Years of experience in designing, deploying, and managing scalable, highly available, and fault-tolerant cloud infrastructure. Expertise in AWS services such as EC2, S3, RDS, Lambda, Redshift, Glue, SageMaker, OpenSearch, and CloudFormation. Strong hands-on experience in Infrastructure as Code (IaC) using Terraform, CloudFormation, and Ansible for automation and configuration management. Proven ability to implement CI/CD pipelines with Jenkins, GitHub Actions, AWS CodePipeline, and CodeDeploy for seamless software deployment. Skilled in containerization and orchestration using Docker, Kubernetes (EKS), and ECS to optimize microservices architecture. Extensive experience in monitoring and logging using Azure Monitor, Log Analytics, AWS CloudWatch, Prometheus, Grafana, and ELK/OpenSearch for proactive alerting and troubleshooting. Designed, built, and maintained CI/CD pipelines to automate build, test, and deployment of MDM and data integration applications, reducing manual effort and release cycles. Proficient in security best practices including IAM, KMS, WAF, Shield, VPC security, and compliance requirements such as HIPAA. Experience in AI and ML-powered DevOps analytics, implementing predictive monitoring and automated self-healing infrastructure using SageMaker, AWS Bedrock, and OpenSearch. Worked with DNS, HTTP/HTTPS, and TCP/IP to troubleshoot and secure internet-facing applications. Monitored and responded to real-time cyber-attacks including DDoS, web application, and bot-based threats. Supported and managed enterprise data platforms involving Oracle, Informatica MDM, APIs, and integration tools, ensuring high availability and data consistency. Expertise in cloud platforms across Azure and AWS, including Azure DevOps, Databricks, Azure Data Factory, ADLS Gen2, and AWS EC2, S3, RDS, Lambda, Redshift, and Glue
Aditya college of engineering
B.Tech · ECE
August 1, 2019 – June 30, 2019
Persistent Systems
Senior Engineering Lead
September 1, 2025 – Present
India
NTT DATA
Senior Consultant
April 1, 2025 – September 1, 2025
India
RLabs Enterprise Services
Linux Application Support
March 1, 2023 – March 1, 2025
India
Techbeez Software Technologies Pvt.Ltd
Linux System Administrator
July 1, 2019 – February 1, 2023
India
Persistent Systems
September 1, 2025 – Present
A large-scale Healthcare CRO (Contract Research Organization) data platform designed to ingest, process, and analyze clinical, operational, and financial datasets in a secure and compliant Azure environment. The platform supports high-volume data pipelines, advanced analytics, and automated deployments while ensuring regulatory compliance (GxP, HIPAA) and high availability. Designed, implemented, and maintained end-to-end CI/CD pipelines using Azure DevOps (ADO) for Databricks notebooks, Azure Data Factory (ADF) pipelines, and infrastructure-as-code.
NTT DATA
April 1, 2025 – September 1, 2025
Allkast is an AI-driven DevOps monitoring and analytics platform that processes complex system logs, infrastructure metrics, and application performance data in real-time. It leverages machine learning (ML) models to predict system failures, optimize CI/CD pipelines, and enhance cloud infrastructure performance. Designed and deployed ML-driven anomaly detection in AWS cloud infrastructure. Implemented AI-based log analytics using OpenSearch (Elasticsearch) and AWS Bedrock. Built automated DevOps workflows using Terraform, Ansible, and AWS CodePipeline. Deployed real-time monitoring and auto-healing with AWS Lambda and Step Functions. Integrated ML-powered predictive analytics using SageMaker and AI-driven autoscaling policies. Managed EC2 instances, creating AMI, snapshots, and scaling using Auto Scaling Groups. Implemented Ansible for server management and automated build/configuration of new instances. Implemented monitoring, logging, and alerting to proactively identify performance bottlenecks and system reliability issues. Configured and maintained Jenkins for CI/CD, automating deployments via Jenkins plugins. Optimized system performance through configuration tuning, pipeline optimization, and automation enhancements. Utilized Python to automate repetitive support activities like log collection, file transfers, and system monitoring. Analyzed traffic patterns to identify malicious activity and applied effective mitigation strategies. Enforced version control, automated testing, and continuous monitoring to improve code quality and deployment stability. Developed self-healing infrastructure using AWS Auto Scaling, Lambda, and EventBridge. Implemented cost optimization strategies using AI-driven forecasting on AWS Cost Explorer. Integrated Python scripts with AWS services (via Boto3) to manage EC2, S3, and IAM tasks.
RLabs Enterprise Services
March 1, 2023 – March 1, 2025
Insistack is a cloud-based analytics platform designed to process and analyze large-scale data for business intelligence. The platform provides real-time analytics, predictive insights, and data visualization solutions using AWS services and DevOps automation. Designed and deployed a serverless data pipeline using AWS Glue, Lambda, and Kinesis. Implemented real-time and batch processing using Apache Kafka and Spark on AWS EMR. Developed CI/CD automation for ETL workflows and machine learning models using Jenkins and AWS CodePipeline. Managed containerized microservices for analytics APIs using Kubernetes (EKS) and Docker. Acted as a technical SME for DevOps tools and processes, providing support and guidance to cross-functional teams. Ensured compliance with security standards, access controls, and data governance policies for primary data systems. Contributed to Agile/Scrum ceremonies, supporting iterative delivery, sprint planning, and continuous improvement initiatives. Implemented data security best practices using AWS KMS, IAM, and encryption policies. Automated monitoring and logging with AWS CloudWatch, ELK Stack, and Prometheus. Integrated data visualization tools like Amazon QuickSight and Grafana for business reporting. Assisted in creating custom health-check utilities using Python for faster issue detection and resolution. Optimized AWS cost and performance using auto-scaling, S3 Intelligent-Tiering, and Spot Instances.
Techbeez software technologies Pvt Ltd
August 1, 2021 – February 1, 2023
DomoData is a data warehousing and cloud infrastructure solution tailored for diverse industry domains. The focus was on building secure, scalable, and high-performance storage and analytics platforms for enterprise-level data processing on Microsoft Azure using DevOps best practices. Architected and deployed enterprise-scale data warehousing solutions on Microsoft Azure using Azure SQL Data Warehouse (Synapse Analytics) and Data Lake Storage. Designed and implemented ETL pipelines using Azure Data Factory and Apache Airflow for both batch and real-time processing. Optimized database performance using Azure SQL Database, Cosmos DB, and indexing strategies. Automated infrastructure provisioning using Terraform and ARM templates integrated with Azure DevOps Pipelines. Implemented data governance and encryption using Azure Key Vault, Azure Information Protection, and RBAC. Deployed automated cost monitoring and optimization using Azure Cost Management and Budgets. Enabled high availability and disaster recovery using Azure Backup, Geo-redundant storage (GRS), and SQL failover groups. Configured Azure networking components including VNets, subnets, route tables, NSGs, and Azure Firewall. Identified and mitigated OWASP Top 10 vulnerabilities impacting web services. Implemented secure DevOps practices adhering to SOC 2, ISO 27001, PCI DSS, and HIPAA compliance standards.
VitalConnect
July 1, 2019 – August 1, 2021
VitalConnect is a patient remote health monitoring application leveraging GCP cloud infrastructure. It ensures secure, real-time health data transmission and monitoring using modern DevOps practices. The project involves deploying scalable and resilient healthcare applications using Infrastructure as Code (IaC) and automation tools. Designed and implemented GCP cloud infrastructure for real-time health monitoring. Utilized Terraform and Ansible for Infrastructure as Code (IaC) and configuration management. Managed CI/CD pipelines using Cloud Build, Cloud Deploy, and GitHub Actions. Ensuring HIPAA compliance and security is the best practice for patient data protection. Monitored system performance using Stackdriver (Cloud Monitoring), Prometheus, and Grafana. Automated deployment and scaling of microservices using Kubernetes (GKE). Implemented log management with ELK Stack (Elasticsearch, Logstash, Kibana). Managed storage-related tasks including persistent disk expansion and Cloud Storage management. Collaborated with internal teams and communicated clear incident updates to customers. Created and managed custom images, snapshots, and disk upgrades for GCP resources. Configured security best practices using IAM roles, service accounts, VPC firewall rules, and encryption mechanisms.
Cultural Fit Analysis
The candidate has worked across diverse industries (Healthcare, AI/ML platforms, Data Warehousing) and with various cloud providers, demonstrating adaptability and a broad technical perspective. The focus on automation, optimization, and compliance aligns well with modern DevOps culture. The breadth of technologies and project types suggests a proactive and continuous learning mindset, which is a strong cultural fit for a Lead DevOps role.
Soft Skills & Operational Fit
The candidate's resume highlights collaboration with data engineering, analytics, and development teams, indicating good teamwork and communication skills. Experience in Agile/Scrum ceremonies and acting as a technical SME suggests leadership potential and ability to guide cross-functional teams. Problem-solving skills are also explicitly mentioned.