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

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:
Bachelors 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 riskbased decision making.