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.