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

Job description

**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.