DevOps

Role Overview:

Virtana is looking for a Senior DevOps Engineer to join our R&D Infrastructure team. In this role, you won't just follow conventions — you'll help redefine them. You will own the architecture, build, and day-to-day operations of the GCP-based cloud platform that powers Virtana's SaaS products and the AI-driven observability experience our Global 2000 customers depend on. This is a hands-on senior individual contributor role with meaningful technical leadership scope, working alongside engineers and architects on a unified observability platform.

Work Location: Pune

Job Type: Hybrid

Role Responsibilities:

• GCP Cloud Operations: Develop, deploy, operate, and support production cloud infrastructure primarily on GCP — leveraging GKE, BigTable, BigQuery, Dataflow, Cloud Storage, IAM, and core networking services.
• Reliability & SLAs: Ensure production systems are running at all times with multiple levels of redundancy to meet committed SLAs; lead incident response, root cause analysis, and post-incident reviews.
• Build & Release Automation: Design, implement, and continuously improve scalable CI/CD pipelines and test frameworks leveraged by QA and development teams across the company.
• Infrastructure as Code: Manage large-scale, repeatable deployments using Terraform, Ansible, Puppet, or SaltStack; champion Git-based workflows and version control standards for distributed engineering teams.
• Security & Availability: Maintain the ongoing maintenance, security, patching, and availability of services in line with tight operations, security, and procedural models.
• Monitoring & Alerting: Plan and deliver high-value monitoring and alerting features to support operations, support, and customer-facing reliability — eating our own dog food with the Virtana Platform wherever possible.
• Capacity & Cost: Forecast capacity, plan upgrades, patches, and migrations, and drive cloud cost efficiency across hybrid and multi-cloud environments.
• Cross-Functional Partnership: Work with development, operations, and support personnel to identify, isolate, and diagnose issues; handle support escalations and drive permanent fixes.

Required Qualifications:

• Bachelor's degree in Computer Science / Engineering or equivalent relevant experience.
• 5–7 years of professional hands-on DevOps / SRE experience supporting production cloud environments.
• Strong, demonstrable production experience on GCP — including GKE, BigTable, BigQuery, Dataflow, IAM, and core GCP networking services.
• Deep, hands-on expertise with container orchestration (Kubernetes) and Docker in production.
• Advanced proficiency with at least one infrastructure-as-code / configuration management tool: Terraform, Ansible, Puppet, or SaltStack.
• Solid understanding of networking, firewalls, load balancers, DNS, and database operations.
• Strong working knowledge of Git-based workflows and version control standards for distributed engineering teams.
• Comfort operating hybrid environments that include both Linux and Windows ecosystems.
• Excellent verbal and written communication skills, with the ability to explain highly technical topics to both technical and non-technical audiences.
• Self-motivated, detail-oriented, and able to work both independently and within a globally distributed team.

Good to Have:

• Strong scripting skills and a demonstrated ability to automate operational toil — Python preferred; Bash, Go, or Groovy a plus.
• Hands-on experience designing and operating CI/CD pipelines with Jenkins (Spinnaker, GitHub Actions, or GitLab CI also welcome).
• Exposure to AWS or other public clouds in addition to GCP.
• Experience operating SaaS platforms built on microservices architectures.

• Experience using AWS (that’s just common sense)
• Experience designing and building web environments on AWS, which includes working with services like EC2, ELB, RDS, and S3
• Experience building and maintaining cloud-native applications
• A solid background in Linux/Unix and Windows server system administration
• Experience using https://www.simplilearn.com/tutorials/devops-tutorial/devops-tools" target="_blank">DevOps tools in a cloud environment, such as Ansible, Artifactory, https://www.simplilearn.com/tutorials/docker-tutorial/what-is-docker-container" target="_blank">Docker, GitHub, https://www.simplilearn.com/tutorials/jenkins-tutorial/what-is-jenkins" target="_blank">Jenkins, https://www.simplilearn.com/tutorials/kubernetes-tutorial/what-is-kubernetes" target="_blank">Kubernetes, Maven, and Sonar Qube
• Experience installing and configuring different application servers such as JBoss, Tomcat, and WebLogic
• Experience using monitoring solutions like CloudWatch, ELK Stack, and Prometheus
• An understanding of writing Infrastructure-as-Code (IaC), using tools like CloudFormation or Terraform
• Knowledge of one or more of the most-used programming languages available for today’s cloud computing (i.e., SQL data, XML data, R math, Clojure math, Haskell functional, Erlang functional, Python procedural, and Go procedural languages)
• Experience in troubleshooting distributed systems
• Proficiency in script development and scripting languages
• The ability to be a team player
• The ability and skill to train other people in procedural and technical topics
• Strong communication and collaboration skills

As a special aside, an AWS engineer who works in DevOps should also have experience with:

• The theory, concepts, and real-world application of Continuous Delivery (CD), which requires familiarity with tools like AWS CodeBuild, AWS CodeDeploy, and AWS CodePipeline
• An understanding of automation

ROLE & RESPONSIBILITIES:

We are hiring a Senior DevSecOps / Security Engineer with 8+ years of experience securing AWS cloud, on-prem infrastructure, DevOps platforms, MLOps environments, CI/CD pipelines, container orchestration, and data/ML platforms. This role is responsible for creating and maintaining a unified security posture across all systems used by DevOps and MLOps teams — including AWS, Kubernetes, EMR, MWAA, Spark, Docker, GitOps, observability tools, and network infrastructure.

KEY RESPONSIBILITIES:

1.     Cloud Security (AWS)-

• Secure all AWS resources consumed by DevOps/MLOps/Data Science: EC2, EKS, ECS, EMR, MWAA, S3, RDS, Redshift, Lambda, CloudFront, Glue, Athena, Kinesis, Transit Gateway, VPC Peering.
• Implement IAM least privilege, SCPs, KMS, Secrets Manager, SSO & identity governance.
• Configure AWS-native security: WAF, Shield, GuardDuty, Inspector, Macie, CloudTrail, Config, Security Hub.
• Harden VPC architecture, subnets, routing, SG/NACLs, multi-account environments.
• Ensure encryption of data at rest/in transit across all cloud services.

2.     DevOps Security (IaC, CI/CD, Kubernetes, Linux)-

Infrastructure as Code & Automation Security:

• Secure Terraform, CloudFormation, Ansible with policy-as-code (OPA, Checkov, tfsec).
• Enforce misconfiguration scanning and automated remediation.

CI/CD Security:

• Secure Jenkins, GitHub, GitLab pipelines with SAST, DAST, SCA, secrets scanning, image scanning.
• Implement secure build, artifact signing, and deployment workflows.

Containers & Kubernetes:

• Harden Docker images, private registries, runtime policies.
• Enforce EKS security: RBAC, IRSA, PSP/PSS, network policies, runtime monitoring.
• Apply CIS Benchmarks for Kubernetes and Linux.

Monitoring & Reliability:

• Secure observability stack: Grafana, CloudWatch, logging, alerting, anomaly detection.
• Ensure audit logging across cloud/platform layers.

3.     MLOps Security (Airflow, EMR, Spark, Data Platforms, ML Pipelines)-

Pipeline & Workflow Security:

• Secure Airflow/MWAA connections, secrets, DAGs, execution environments.
• Harden EMR, Spark jobs, Glue jobs, IAM roles, S3 buckets, encryption, and access policies.

ML Platform Security:

• Secure Jupyter/JupyterHub environments, containerized ML workspaces, and experiment tracking systems.
• Control model access, artifact protection, model registry security, and ML metadata integrity.

Data Security:

• Secure ETL/ML data flows across S3, Redshift, RDS, Glue, Kinesis.
• Enforce data versioning security, lineage tracking, PII protection, and access governance.

ML Observability:

• Implement drift detection (data drift/model drift), feature monitoring, audit logging.
• Integrate ML monitoring with Grafana/Prometheus/CloudWatch.

4.     Network & Endpoint Security-

• Manage firewall policies, VPN, IDS/IPS, endpoint protection, secure LAN/WAN, Zero Trust principles.
• Conduct vulnerability assessments, penetration test coordination, and network segmentation.
• Secure remote workforce connectivity and internal office networks.

5.     Threat Detection, Incident Response & Compliance-

• Centralize log management (CloudWatch, OpenSearch/ELK, SIEM).
• Build security alerts, automated threat detection, and incident workflows.
• Lead incident containment, forensics, RCA, and remediation.
• Ensure compliance with ISO 27001, SOC 2, GDPR, HIPAA (as applicable).
• Maintain security policies, procedures, RRPs (Runbooks), and audits.

IDEAL CANDIDATE:

• 8+ years in DevSecOps, Cloud Security, Platform Security, or equivalent.
• Proven ability securing AWS cloud ecosystems (IAM, EKS, EMR, MWAA, VPC, WAF, GuardDuty, KMS, Inspector, Macie).
• Strong hands-on experience with Docker, Kubernetes (EKS), CI/CD tools, and Infrastructure-as-Code.
• Experience securing ML platforms, data pipelines, and MLOps systems (Airflow/MWAA, Spark/EMR).
• Strong Linux security (CIS hardening, auditing, intrusion detection).
• Proficiency in Python, Bash, and automation/scripting.
• Excellent knowledge of SIEM, observability, threat detection, monitoring systems.
• Understanding of microservices, API security, serverless security.
• Strong understanding of vulnerability management, penetration testing practices, and remediation plans.

EDUCATION:

• Master’s degree in Cybersecurity, Computer Science, Information Technology, or related field.
• Relevant certifications (AWS Security Specialty, CISSP, CEH, CKA/CKS) are a plus.

PERKS, BENEFITS AND WORK CULTURE:

• Competitive Salary Package
• Generous Leave Policy
• Flexible Working Hours
• Performance-Based Bonuses
• Health Care Benefits

Roles and Responsibilities:

• AWS Cloud Management: Design, deploy, and manage AWS cloud infrastructure. Optimize and maintain cloud resources for performance and cost efficiency. Monitor and ensure the security of cloud-based systems.
• Automated Provisioning: Develop and implement automated provisioning processes for infrastructure deployment. Utilize tools like Terraform and Packer to automate and streamline the provisioning of resources.
• Infrastructure as Code (IaC): Champion the use of Infrastructure as Code principles. Collaborate with development and operations teams to define and maintain IaC scripts for infrastructure deployment and configuration.
• Collaboration and Communication: Work closely with cross-functional teams to understand project requirements and provide DevOps expertise. Communicate effectively with team members and stakeholders regarding infrastructure changes, updates, and improvements.
• Continuous Integration/Continuous Deployment (CI/CD): Implement and maintain CI/CD pipelines to automate software delivery processes. Ensure reliable and efficient deployment of applications through the development lifecycle.
• Performance Monitoring and Optimization: Implement monitoring solutions to track system performance, troubleshoot issues, and optimize resource utilization. Proactively identify opportunities for system and process improvements. 

Mandatory Skills:

• Proven experience as a DevOps Engineer or similar role, with a focus on AWS.
• Strong proficiency in automated provisioning and cloud management.
• Experience with Infrastructure as Code tools, particularly Terraform and Packer.
• Solid understanding of CI/CD pipelines and version control systems.
• Strong scripting skills (e.g., Python, Bash) for automation tasks.
• Excellent problem-solving and troubleshooting skills.
• Good interpersonal and communication skills for effective collaboration.

Secondary Skills:

• AWS certifications (e.g., AWS Certified DevOps Engineer, AWS Certified Solutions Architect).
• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Knowledge of microservices architecture and serverless computing.
• Familiarity with monitoring and logging tools (e.g., CloudWatch, ELK stack).

Hands on Experience with Linux administration

Experience using Python or Shell scripting (for Automation) 

Hands-on experience with Implementation of CI/CD Processes 

Experience working  with one cloud platforms (AWS or Azure or Google) 

Experience working with configuration management tools such as Ansible & Chef 

Experience working with  Containerization tool Docker.

Experience working with Container Orchestration tool Kubernetes.

Experience in source Control Management including SVN and/or Bitbucket

& GitHub

Experience with setup & management of monitoring tools like Nagios, Sensu & Prometheus or any other popular tools 

Hands-on experience in Linux, Scripting Language & AWS is mandatory

Troubleshoot and Triage development, Production issues

Job Dsecription:

○ Develop best practices for team and also responsible for the architecture

○ solutions and documentation operations in order to meet the engineering departments quality and standards

○ Participate in production outage and handle complex issues and works towards Resolution

○ Develop custom tools and integration with existing tools to increase engineering Productivity

Required Experience and Expertise

○ Having a good knowledge of Terraform + someone who has worked on large TF code bases.

○ Deep understanding of Terraform with best practices & writing TF modules.

○ Hands-on experience of GCP  and AWS and knowledge on AWS Services like VPC and VPC related services like (route tables, vpc endpoints, privatelinks) EKS, S3, IAM. Cost aware mindset towards Cloud services.

○ Deep understanding of Kernel, Networking and OS fundamentals

NOTICE PERIOD - Max - 30 days