DevOps Engineer / Cloud Architect AWS

Cognizant; Kolkata District, Chennai District, Bengaluru; Hybrid; INR 3,500,000 - 6,000,000; Full time

Job summary

Cognizant in India seeks a senior cloud architect to design and own cloud-native platform infrastructure across AWS, Kubernetes, and AI integrations. You will lead IaC, security, cost, and observability while guiding engineers and partnering with security and AI teams.

You will define scalable data and compute topologies, build CI/CD pipelines, and mentor teams on best practices in cloud, DevOps, and SRE to deliver resilient, cost-aware solutions.

Qualifications

• Experience designing cloud architectures for scalable, secure platforms on AWS.
• Strong background in IaC, CI/CD, and automation across dev/prod.
• Exposure to AI/ML integrations (Bedrock, SageMaker) is a plus.

Responsibilities

• Design and own target-state cloud architecture across compute, network, storage, and security.
• Define topology for Kubernetes, containers, databases, and object storage; guide build-out.
• Architect and improve CI/CD pipelines and source-control workflows for backend and AI services.
• Design identity and access strategy with least privilege at scale.
• Establish observability strategy with logging, monitoring, and tracing.
• Integrate AI/RAG components with data and API layers.
• Lead root-cause analysis for deployment and performance issues across layers.
• Create standards, reference architectures, and reusable deployment modules.

Skills

Cloud architecture

DevOps

Security best practices

Cost optimization

IaC

Team mentorship

Tools

AWS

Kubernetes

Terraform

Docker

OpenSearch Serverless

SageMaker

RDS PostgreSQL

DynamoDB

S3

EC2

API Gateway

Route 53

Bedrock

IAM

Secrets Manager

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 EKS cluster, node groups, and supporting VPC/subnet topology.
• Architect the data layer using RDS (PostgreSQL) and DynamoDB, along with S3 buckets for build artifacts, infrastructure-as-code state, and knowledge-base content.
• Set up ECR repositories, Lambda functions, and private API Gateway endpoints for application, agent, and metrics services.
• Design DNS and ingress via Route 53 and Application Load Balancer, and configure required VPC endpoints (STS, ECR, RDS, Lambda, Secrets Manager, Bedrock, S3, AOSS, etc.).
• Design and implement IAM roles and policies for Bedrock agents, build/pipeline services, Lambda, and knowledge-base access, following least‑privilege scoping.
• Architect the integration of Amazon Bedrock (Guardrails, Knowledge Bases, Agents) and OpenSearch Serverless / Aurora PostgreSQL for RAG and semantic search.
• Define CloudWatch logging/monitoring and dashboards for platform and AI metrics, and review the architecture periodically for cost, security, and resilience improvements.

Required Technical Skills Amazon Web Services (AWS)

Compute & Orchestration: EKS, EC2 (jump host), Kubernetes Data & Storage: RDS (PostgreSQL), DynamoDB, S3, Aurora PostgreSQL (vector store, optional) Serverless & Integration: AWS Lambda, Amazon API Gateway, Amazon Route 53 AI / ML: Amazon Bedrock (Guardrails, Knowledge Bases, Agents), OpenSearch Serverless (AOSS), SageMaker Security & Identity: IAM roles/policies, AWS Secrets Manager, SSM Parameter Store, SSO (e.g., Azure AD / AWS Cognito) CI/CD: AWS CodePipeline, AWS CodeBuild, AWS CodeConnections, Git-based source control Observability: CloudWatch, Container Insights, LLM/AI observability tooling