DevOps Engineer / Cloud Architect GCP

Cognizant; Kolkata District, Chennai District, Bengaluru; Hybrid; INR 1,500,000 - 2,100,000; Full time

Job summary

Cognizant seeks a senior cloud architect to design and own the target-state cloud platform for a cloud-native AI-enabled application. You will define Kubernetes, data, and security topology and translate it into infrastructure-as-code, guiding implementation across CI/CD and tooling.

Responsibilities include building scalable, cost-aware infrastructure, establishing observability, IAM strategy, and AI/RAG integration with data and APIs, while mentoring engineers and aligning with product goals.

Qualifications

• Strong experience designing cloud-native platforms with IaC to support scalable AI-enabled applications.
• Expertise in Kubernetes, data services, networking, security, and observability.
• Proficiency in CI/CD pipelines, source control, and automation for backend/frontend/AI services.

Responsibilities

• Design and own the target-state cloud architecture compute, networking, data, and security layers; translate to infrastructure‑as‑code (Terraform or equivalent).
• Define the Kubernetes, container registry, database, object storage, secrets management, and networking topology; guide build-out.
• Architect and continuously improve CI/CD pipelines and source‑control workflows to automate build, test, and deployment of backend, frontend, and AI‑agent services.
• Design identity and access strategy (RBAC/IAM, managed identities, key/secret stores) with least privilege.
• Define observability strategy — logging, monitoring, tracing, and alerting for application, container, and AI/LLM runtime metrics.
• Architect integration of AI/RAG components with platform data and API layers.
• Lead root‑cause analysis and resolve complex deployment, connectivity, and performance issues across compute, network, and data layers.
• Establish infrastructure standards, reference architectures, and reusable modules; maintain deployment documentation and environment configuration.
• Evaluate architecture for cost efficiency, scalability, resilience, and disaster recovery; recommend improvements.
• Partner with application, security, and AI leads to align infra decisions with business goals; mentor engineers.
• Design and provision the GKE cluster and supporting VPC/subnet topology.
• Architect the data layer using AlloyDB, GCS, and Artifact Registry for app/images.
• Set up Cloud Run services for application logic, APIs, and backend services.
• Design DNS and ingress via Cloud DNS and Load Balancer, and configure Private Service Connect / Private Google Access.
• Design secrets strategy using Secret Manager and Cloud Logging for logs, metrics, tracing.
• Architect a RAG pipeline using Cloud Dataflow and deploy AI/ML services on Vertex AI.
• Establish IaC standards across landing-zone, infra provisioning, and app provisioning modules.
• Architect Cloud Build pipelines for CI/CD automation and review architecture for cost, security, and resilience.

Skills

Cloud architecture

DevOps practices

CI/CD design

Security best practices

IaC (Terraform)

Tools

GKE

Kubernetes

Compute Engine VM

AlloyDB

Google Cloud Storage (GCS)

Cloud Run

Artifact Registry

VPC

Private Service Connect

Private Google Access

Cloud DNS

Load Balancer

Ingress

Vertex AI

Gemini models

Cloud Dataflow

Secret Manager

GCP Service Accounts

IAM

SSO integration

Cloud Build

Terraform

Git

Node.js

PostgreSQL

Job description

Key Responsibilities

• Design and own the target-state cloud architecture compute, networking, data, and security layers for a cloud‑native, AI‑enabled application platform, and translate it into infrastructure‑as‑code (Terraform or equivalent).
• Define the Kubernetes, container registry, database, object storage, secrets management, and networking topology, and guide implementation teams through build‑out.
• Architect and continuously improve CI/CD pipelines and source‑control workflows to automate build, test, and deployment of backend, frontend, and AI‑agent services.
• Design identity and access strategy (RBAC/IAM, managed identities, key/secret stores) that enforces least privilege while remaining operable at scale.
• Define the observability strategy — logging, monitoring, tracing, and alerting — for application, container, and AI/LLM runtime metrics.
• Architect the integration of AI/RAG components (knowledge bases, agents, vector search, model endpoints) with the platform's data and API layers.
• Lead root‑cause analysis and drive resolution of complex deployment, connectivity, and performance issues across compute, network, and data layers.
• Establish infrastructure standards, reference architectures, and reusable modules; maintain deployment documentation, runbooks, and environment configuration across dev/test/prod.
• Evaluate architecture for cost efficiency, scalability, resilience, and disaster recovery, and recommend improvements.
• Partner with application, security, and AI engineering leads to align infrastructure decisions with product and business goals, and mentor engineers on cloud and DevOps best practices.
• Design and provision the GKE cluster and supporting VPC/subnet topology (private/public subnets, plus additional subnets for data pipelines, agents, serverless, and private build pools).
• Architect the data layer using AlloyDB (PostgreSQL-compatible), Google Cloud Storage (GCS), and Artifact Registry for application and function container images.
• Set up Cloud Run services for application logic, APIs, and backend services.
• Design DNS and ingress via Cloud DNS and Load Balancer, and configure Private Service Connect / Private Google Access and GCP Service Accounts.
• Design the secrets and credential strategy using Secret Manager, and define Cloud Logging for logs, metrics, and tracing.
• Architect a RAG pipeline using Cloud Dataflow, and deploy/scale AI-ML services on Vertex AI, confirming regional compatibility across services.
• Establish infrastructure‑as‑code standards, backend configs, and environment variable structures across landing‑zone, infra‑provisioning, and app‑provisioning modules.
• Architect Cloud Build pipelines for CI/CD automation, and review the architecture periodically for cost, security, and resilience improvements.

Required Technical Skills

Google Cloud Platform (GCP) Compute & Orchestration: GKE, Kubernetes, Compute Engine VM (jump host) Data & Storage: AlloyDB (PostgreSQL-compatible), Google Cloud Storage (GCS) Serverless & Integration: Cloud Run, Artifact Registry Networking: VPC, Private Service Connect, Private Google Access, Cloud DNS, Load Balancer / Ingress AI / ML: Vertex AI, Gemini models, Cloud Dataflow (RAG pipeline) Security & Identity: Secret Manager, GCP Service Accounts, IAM, SSO integration CI/CD: Cloud Build, Infrastructure‑as‑code tooling, Git‑based source control Runtime / Dev Portal: Internal developer portal tooling, Node.js, PostgreSQL