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 setting 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

📌 DevOps Engineer / Cloud Architect AWS (Kolkata)
🏢 Cognizant
📍 Kolkata