DevOps Architect

We’re Hiring | DevOps Architect – Purple Fabric (Intellect)
Job Title: DevOps Architect
Business Unit: Purple Fabric (PF)
Experience: 12–18 Years
Job Location: Chennai, India
Work Model: On-site
About Purple Fabric
Purple Fabric (PF) is Intellect's enterprise AI and decisioning platform, designed to help organizations build, orchestrate, govern, and scale AI-powered solutions. It enables enterprise adoption of Agentic AI, intelligent automation, and data-driven decisioning, with a focus on security, governance, scalability, and operational reliability.
We are looking for an experienced DevOps Architect to design, build, and optimize the infrastructure and platform engineering capabilities that support Purple Fabric's enterprise AI workloads. The ideal candidate will bring deep expertise in Kubernetes, AWS, cloud-native architecture, CI/CD automation, infrastructure as code, observability, and enterprise platform operations.
Role Overview
As a DevOps Architect, you will own the architecture and implementation of scalable, secure, resilient, and automated infrastructure for Purple Fabric. You will work closely with Platform Engineering, AI/ML Engineering, Software Engineering, Security, and Product teams to establish reliable deployment and operational practices across development, testing, and production environments.
The role requires a strong hands-on technical leader who can define platform architecture, establish DevOps standards, troubleshoot complex distributed systems, and drive automation and operational excellence across cloud and on-premises environments.
Key Responsibilities
1. Cloud-Native Architecture and Platform Engineering
Design and implement scalable, highly available, secure, and resilient cloud-native infrastructure for enterprise applications and AI platforms.
Architect Kubernetes-based platforms, including cluster design, node management, workload scheduling, networking, storage, ingress, autoscaling, and high availability.
Define architecture standards for containerized applications, microservices, service discovery, configuration management, and secrets handling.
Design infrastructure patterns that support multi-environment deployments, workload isolation, disaster recovery, and business continuity.
Evaluate and implement platform engineering practices that improve developer productivity, deployment consistency, and operational reliability.
2. AWS Cloud Infrastructure
Design, implement, and optimize cloud infrastructure on AWS for enterprise-scale applications and platform workloads.
Work with AWS services such as EKS, EC2, VPC, IAM, S3, CloudWatch, Route 53, and load-balancing services, as appropriate to the solution architecture.
Define cloud networking, security, access control, resource provisioning, and cost-optimization strategies.
Architect reliable and scalable environments across development, staging, and production.
Support hybrid and on-premises deployments where required by customer or platform needs.
3. Kubernetes and Container Orchestration
Lead Kubernetes architecture and administration, including cluster provisioning, upgrades, capacity planning, node pools, networking, storage, and resource optimization.
Establish standards for Docker/container images, Helm charts, Kubernetes manifests, and deployment configurations.
Implement autoscaling, resource quotas, health checks, service discovery, and workload resilience.
Troubleshoot complex issues involving pods, nodes, cluster networking, persistent volumes, scheduling, and application performance.
Define Kubernetes security practices, including RBAC, network policies, secrets management, and container image scanning.
4. CI/CD and DevOps Automation
Architect and maintain CI/CD pipelines using tools such as Jenkins, GitLab CI/CD, GitHub Actions, or equivalent platforms.
Automate build, test, security scanning, packaging, deployment, rollback, and release-management processes.
Implement GitOps and declarative deployment practices where appropriate.
Establish standardized deployment strategies, including rolling, blue-green, and canary releases.
Integrate automated quality checks, vulnerability scanning, and policy enforcement into delivery pipelines.
5. Infrastructure as Code and Configuration Management
Design and implement infrastructure provisioning using Terraform, CloudFormation, Ansible, or equivalent tools.
Establish reusable infrastructure modules, version-controlled configurations, and environment-promotion practices.
Automate infrastructure lifecycle management, configuration drift detection, and repeatable environment provisioning.
Implement policy-as-code and infrastructure compliance checks where appropriate.
Improve provisioning speed, consistency, and operational efficiency through automation.
6. Observability, Reliability, and Performance Engineering
Design end-to-end monitoring, logging, tracing, alerting, and observability frameworks.
Implement and optimize monitoring solutions using Prometheus, Grafana, ELK/OpenSearch, CloudWatch, or equivalent technologies.
Define and monitor service-level indicators, service-level objectives, availability, latency, and error rates.
Lead root-cause analysis, incident response, capacity planning, and performance optimization.
Establish disaster recovery, backup, resilience testing, and operational readiness practices.
7. Security and Enterprise Governance
Embed DevSecOps principles throughout the software delivery lifecycle.
Implement identity and access management, least-privilege access, secrets management, encryption, and secure network architecture.
Integrate container, dependency, and infrastructure vulnerability scanning into CI/CD pipelines.
Support auditability, compliance requirements, security reviews, and enterprise deployment standards.
Collaborate with security teams to establish guardrails for cloud infrastructure and platform operations.
8. AI Platform and Workload Enablement
Build and operate reliable infrastructure to support AI/ML services, LLM-powered applications, agentic workflows, and associated enterprise integrations.
Collaborate with AI/ML and platform teams to support model-serving services, API gateways, asynchronous workloads, and scalable application deployments.
Design deployment and operational patterns for AI services, considering resource utilization, scalability, latency, availability, and cost.
Support secure deployment models for enterprise customers, including isolated environments and private or on-premises deployments where applicable.
Establish observability and operational practices for AI application services and their supporting infrastructure.
9. Technical Leadership and Stakeholder Collaboration
Define DevOps architecture standards, reference designs, best practices, and technical roadmaps.
Mentor DevOps and platform engineering teams and provide technical direction for complex infrastructure initiatives.
Work closely with Engineering, Architecture, Security, QA, Product, and Delivery teams to align platform capabilities with product requirements.
Drive technical reviews, architecture decisions, troubleshooting sessions, and continuous improvement initiatives.
Evaluate emerging tools and technologies and recommend solutions based on scalability, security, maintainability, and business value.
Required Skills and Qualifications
12–18 years of experience in DevOps, Cloud Infrastructure, SRE, Platform Engineering, or related technology domains.
Strong hands-on expertise in Kubernetes and AWS.
Proven experience designing and operating Kubernetes clusters and production-grade containerized platforms.
Strong understanding of AWS networking, IAM, compute, storage, monitoring, and security services.
Expertise in CI/CD pipeline design, release automation, and DevSecOps practices.
Hands-on experience with Infrastructure as Code, preferably Terraform, CloudFormation, or equivalent tools.
Strong experience with Docker, container registries, Helm, and Kubernetes deployment patterns.
Good understanding of Linux administration, networking, DNS, TLS, load balancing, and distributed systems.
Experience implementing monitoring, logging, alerting, and observability solutions.
Strong troubleshooting skills across infrastructure, application deployment, networking, and performance issues.
Experience defining architecture standards, leading technical initiatives, and mentoring engineering teams.
Excellent communication and collaboration skills, with the ability to work across technical and business stakeholders.
Preferred Qualifications
Experience supporting enterprise AI platforms, LLM-based applications, AI/ML workloads, or Agentic AI platforms.
Exposure to model-serving infrastructure, GPU-enabled workloads, or AI inference infrastructure.
Experience with hybrid cloud, private cloud, or on-premises deployments.

Work arrangement
No