Key Responsibilities

Cloud Architecture Engineering (AWS):

Design, build, and operate scalable, secure, highly available AWS workloads (compute, networking, storage, data, serverless).

Develop reference architectures and IaC modules aligned to best practices and guardrails.

DevOps Platform Automation

Implement CI/CD pipelines, automated testing, and

GitOps workflows. Own Infrastructure as Code (Terraform/CDK/CloudFormation), configuration management, and environment provisioning across dev/test/prod.

Observability Reliability

Set up logging, metrics, tracing, and SLOs using CloudWatch.

Drive incident response, postmortems, capacity planning, and reliability improvements.

Security Compliance

Embed security-by-design (IAM, KMS, Secrets Manager), enforce least privilege, and implement threat detection and vulnerability management.

Support compliance needs (e.g., SOC2, ISO 27001, GxP) via policy-as-code and automated controls.

Cost Management FinOps

Monitor and optimize cloud spend with tagging, budgets, RI/SP management, right sizing, and usage analytics. Advise teams on cost efficient architectures.

Data Integration

Build data pipelines (AWS Glue, Step Functions, Lambda, EventBridge) and API integrations (API Gateway, AppSync, ALB/NLB) to support AI workloads and product features.

AI Platform Enablement (Bedrock, GenAI)

Design and operate Amazon Bedrock integrations, model access patterns, prompt and retrieval pipelines, and RAG architectures using AWS native and open tooling.

Agentic AI Orchestration

Implement agentic workflows (tool use, planning, memory) with frameworks (LangChain, AWS Agents for Bedrock) and secure tool adapters (search, code, data).

Manage observation and safety layers.

MLOps For Foundation Models

Establish versioning, evaluation, governance, and rollout practices for prompts, datasets, embeddings, and model variants.

Automate offline/online evaluation, A/B tests, and canary releases.

Cross Functional Collaboration

Partner with product, data science, security, and compliance to translate requirements into robust cloud and AI solutions.

Provide technical documentation and knowledge sharing.

Required Qualifications

Education/Experience:

Bachelor's degree in Computer Science/Engineering or equivalent experience;

Minimum 6-9 Years Of Experience In The IT Industry.

5+ years in cloud engineering/DevOps with 3+ years hands-on in AWS.

AWS Expertise

Proficiency in IAM, VPC, EC2/EKS, Lambda, API Gateway/AppSync,

S3, RDS/Aurora/DynamoDB, CloudWatch, KMS, Secrets Manager, Step Functions, EventBridge, Glue.

DevOps IaC

Strong skills in Terraform (or AWS CDK/CloudFormation), CI/CD

(GitHub Actions/GitLab CI/AWS CodePipeline), containerization (Docker, Kubernetes/EKS), and artifact management.

Security

Solid understanding of cloud security, networking, encryption, key management, least privilege, and policy-as-code (e.g., OPA/AWS Config).

AI Skills

Hands-on with Amazon Bedrock, LLM integration, prompt engineering, RAG pipelines (vector stores like OpenSearch, Aurora, or DynamoDB + embedding), and

agent frameworks (e.g., LangChain, Agents for Bedrock). Experience with model evaluation, guardrails, and content moderation.

MLOps/Governance

Knowledge of versioning (DVC/Git), experiment tracking

(MLflow/SageMaker), feature/embedding stores, A/B testing, and deployment strategies for AI features.

Soft Skills

Strong communication, documentation, collaboration, and ownership mindset. Comfortable working in regulated environments with risk‑based decision making.

AWS GenAI Platform Engineer (Cloud Engineer with AWS + AI)