AWS Solutions Expert

Qualifications

• Masters/Bachelor’s degree in Computer Science, Engineering, or related field.

• 5+ years of hands‑on experience designing and implementing AWS solutions.

• Deep expertise in:

  • Serverless services (Lambda, API Gateway, EventBridge, SQS/SNS)

  • Container orchestration (EKS/Kubernetes)

  • Relational databases (RDS, Aurora)

  • AWS IAM, security controls, encryption, network isolation

• Proven track record building CI/CD pipelines with GitHub Actions and automated testing frameworks.

• Strong scripting skills (Python, Node.js, or similar).

• Excellent communication, documentation, and leadership abilities.

• Exposure to large language models (e.g., OpenAI, Anthropic) and multi-agent architectures.

• Experience with CDK, Terraform, or Serverless Framework.

• Familiarity with observability tools: Prometheus, Grafana, CloudWatch.

• AWS certifications (Solutions Architect Professional, DevOps Engineer).

Responsibilities

• Architecture & Design

  • Lead solutioning for distributed, serverless workloads on AWS (Lambda, API Gateway, EventBridge, SQS, SNS).

  • Design resilient, secure, and cost-optimized architectures using RDS, DynamoDB, Aurora Serverless, and EKS.

• Security & IAM

  • Develop and enforce AWS IAM policies, least-privilege roles, Service Control Policies, and SCPs.

  • Implement security best practices (VPC design, KMS encryption, AWS WAF, Security Hub).

• Infrastructure Automation

  • Build and maintain CI/CD pipelines using GitHub Actions and Workflows.

  • Author Infrastructure-as-Code (IaC) with CloudFormation, Terraform or CDK.

  • Integrate automated testing (unit, integration, security scanning) nto deployment pipelines.

• Operational Excellence

  • Monitor, troubleshoot, and optimize performance of serverless functions and container clusters.

  • Implement logging and observability using CloudWatch, X-Ray, Prometheus, Grafana.

• Innovation & Collaboration

  • Stay current on AWS feature releases; evaluate and adopt new services.

  • Partner with product, data science, and ML engineering teams—especially those building LLM-driven and multi-agent solutions.

  • Mentor junior engineers and share knowledge through documentation and tech talks.