Systems Architect - Cloud AIOps

Epam Systems; Hyderabad, Chennai District, Bengaluru; On-site; INR 4,200,000 - 6,300,000; Full time

Job summary

Epam Systems in Hyderabad seeks an experienced Systems Architect - Cloud AIOps to design and evolve cloud platforms with AI-driven intelligence as a foundational design principle. You will shape AI-enabled platform engineering strategies and lead AIOps architecture initiatives across cloud infrastructure and cloud-native environments.

As a senior technical authority, you will guide modernization programs, implement AI-assisted reliability, and mentor engineers to establish AI-aware standards

Qualifications

• 13–20 years of experience in cloud platform architecture and engineering.
• Expertise in enterprise landing zones, VPC/VNet design, and security & IAM, with hybrid/multi-cloud connectivity.
• For cloud-native path: Kubernetes platforms (EKS/AKS), microservices modernization, and service mesh (Istio/Linkerd).
• Infrastructure as Code using Terraform, Ansible, and CloudFormation/ ARM/Bicep.
• CI/CD and platform automation with Jenkins, GitHub Actions, and GitLab CI.
• Scripting in Python, Shell, and PowerShell for automation.
• Observability stacks such as Prometheus, Grafana, and ELK/EFK, plus OpenTelemetry, Datadog, AppDynamics, New Relic.
• Strong SRE foundations: SLIs/SLOs, error budgets, incident automation, and postmortems.
• Expertise in AIOps: event correlation, anomaly detection, automated remediation, predictive alerting.
• English communication at B2 level or higher.

Responsibilities

• Architect and evolve cloud platforms (AWS or Azure) with AI-driven intelligence as a foundation.
• Define and drive AI-enabled platform engineering strategy, integrating observability and automation.
• Lead AIOps architectures combining monitoring data, logs, metrics, traces and knowledge.
• Embed SRE principles (SLIs, SLOs, error budgets) into platform design for AI-assisted reliability.
• Serve as senior technical authority in customer engagements, architecture workshops, and RFP/RFI responses.
• For cloud infra: design AI-ready landing zones, networking, IAM, hybrid connectivity, DR and cost governance.
• For cloud native: architect Kubernetes platforms and cloud-native runtimes for intelligent operations.
• Design AI-assisted knowledge systems (RAG) to accelerate RCA and decision making.
• Collaborate with data/ML teams to consume AI/ML capabilities for operations.
• Provide architectural guidance on LLM, vector stores, and RAG pipelines.

Skills

Cloud architecture

AIOps

Kubernetes (EKS/AKS)

Terraform

CI/CD tooling

Scripting (Python, Shell, PowerShell)

Observability

SRE fundamentals

RAG/LLM integration

Leadership & mentoring

Tools

Terraform

Ansible

CloudFormation/ARM/Bicep

Prometheus

Grafana

ELK/EFK

Datadog

AppDynamics

New Relic

GitHub Actions

Jenkins

GitLab CI

Job description

We are seeking an experienced Systems Architect - Cloud AIOps to design and evolve next-generation cloud platforms where AI-driven intelligence is embedded as a foundational design principle. In this role, you will shape AI-enabled platform engineering strategies, lead AIOps architecture initiatives, and serve as a senior technical authority guiding enterprise modernization programs across cloud infrastructure and cloud-native environments.

Responsibilities

• Architect and evolve cloud platforms (AWS or Azure) where AI-driven intelligence is a foundational design principle, not an afterthought
• Define and drive AI-enabled platform engineering strategy, integrating observability, telemetry pipelines, intelligent alerting, predictive insights, and automation
• Lead the design of AIOps architectures that combine monitoring data, logs, metrics, traces, and operational knowledge to support proactive operations
• Embed SRE principles (SLIs, SLOs, error budgets) into platform design and enable AI-assisted reliability management
• Serve as a senior technical authority in customer-facing engagements, architecture workshops, and RFP/RFI responses, articulating AI-driven platform value
• For Cloud Infrastructure-led engagements: architect AI-ready landing zones, networking, IAM, hybrid connectivity, DR, and cost governance
• For Cloud Native-led engagements: architect Kubernetes platforms, modernization initiatives, and cloud-native runtime environments designed for intelligent operations
• Design and integrate AI-assisted operational knowledge systems, including RAG-based architectures, to accelerate root cause analysis and decision support
• Collaborate with data and ML teams to consume AI/ML capabilities for operations (anomaly detection, automated RCA, predictive capacity insights)
• Provide architectural guidance on LLM, vector stores, and RAG pipelines as platform components for operational and reliability use cases
• Mentor architects and senior engineers, establishing AI-aware platform engineering standards across the organization

Requirements

• 13-20 years of experience in cloud platform architecture and engineering
• For the Cloud Infrastructure path: expertise in enterprise landing zones, VPC/VNet design, and security & IAM, along with hybrid/multi-cloud connectivity
• For the Cloud Native path: proficiency in Kubernetes platforms (EKS/AKS), microservices modernization, and service mesh (Istio/Linkerd), including GitOps and container security
• Background in Infrastructure as Code using Terraform, Ansible, and CloudFormation or ARM/Bicep
• Skills in CI/CD and platform automation with Jenkins, GitHub Actions, and GitLab CI
• Competency in automation and integration scripting with Python, Shell, and PowerShell
• Familiarity with observability stacks such as Prometheus, Grafana, and ELK/EFK, as well as OpenTelemetry, Datadog, AppDynamics, and New Relic
• Strong SRE foundations covering SLIs/SLOs, error budgets, incident automation, and postmortems
• Expertise in AIOps capabilities, including event correlation across telemetry sources, anomaly detection on metrics/logs/traces, automated remediation with intelligent runbooks, and predictive alerting and capacity insights
• Understanding of RAG architectures for operational knowledge retrieval and AI-assisted decisioning
• Proven experience leading platform-led modernization programs and influencing platform roadmaps at enterprise and portfolio levels
• Capability to assess legacy and cloud environments and define AI-enabled target platform architectures, combining platform engineering, AI capabilities, and operational excellence
• Strong English communication skills (B2 level or higher)