Senior DevOps Engineer (Azure and AWS)

Minimum Experience - 10 yrs

About the Role

We are seeking a highly skilled Cloud & Infrastructure Architect to lead the design, implementation, and optimization of hybrid cloud environments. This role demands deep expertise in cloud platforms (AWS, GCP, Azure), infrastructure automation, DevOps practices, and hybrid integrations. If you're passionate about building scalable, secure, and resilient systems, we’d love to hear from you.

Key Responsibilities

Cloud & Infrastructure Architecture

Design secure, resilient, and high-performance cloud architectures on AWS.

Support integration with GCP, Azure, and on-premise infrastructure (VMware, OpenStack).

Define hybrid cloud strategies covering security, identity federation, and data governance.

Develop infrastructure blueprints and reference architectures aligned with business needs.

Infrastructure as Code & Automation

Champion Infrastructure as Code (IaC) using Terraform, CloudFormation, or Pulumi.

Automate environment provisioning and configuration using Ansible, Helm, etc.

Establish GitOps pipelines for consistency, change tracking, and governance.

DevOps & Continuous Delivery

Architect and manage CI / CD pipelines using GitHub Actions, GitLab CI, Jenkins, or ArgoCD.

Embed security and compliance into the software delivery lifecycle (DevSecOps).

Implement release strategies like blue / green, canary deployments, and feature flagging.

Hybrid Infrastructure & On-Premise Integration

Lead integration of on-prem systems with cloud-native services.

Manage container platforms, virtualized environments, and legacy applications.

Enforce disaster recovery, backup, and failover strategies across hybrid deployments.

Monitoring, SRE & Reliability Engineering

Define and monitor SLAs, SLIs, and SLOs; implement proactive alerting and auto-remediation.

Operationalize observability using Prometheus, Grafana, ELK, CloudWatch, Datadog.

Drive incident response, RCA, and post-mortem processes for continuous improvement.

AI / ML Platform Enablement (Preferred)

Collaborate with data and ML teams to provision infrastructure for AI / ML workloads.

Support orchestration frameworks like Kubeflow, MLflow, Airflow, and cloud-native ML services.

Optimize infrastructure for data ingestion, feature engineering, and real-time inference.

Technical Skills

AWS Certifications : DevOps Engineer – Professional, Solutions Architect – Professional.

Experience with data platforms, AI / ML pipelines, and high-volume data lake architectures.

Familiarity with ITIL, SRE principles, and compliance standards (ISO 27001, SOC2, GDPR).

Expertise in cloud cost optimization and FinOps best practices.

Behavioral Competencies

Strategic Thinking : Ability to define scalable architectures aligned with business growth.

Technical Leadership : Influences cross-functional decisions and promotes engineering excellence.

Security-First Mindset : Prioritizes resilience, auditability, and compliance.

Communication : Comfortable presenting complex technical topics to diverse audiences.