AWS Data & AI Platform Engineer

Compunnel

$120K — $145K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of cloud platform development on AWS
  • 2+ years of hands-on experience with Amazon Bedrock, EKS/Kubernetes, and AWS Lambda
  • 8+ years of Python development for APIs or AI services
  • 5+ years designing secure, scalable distributed systems
  • 5+ years in regulated sectors like financial services or insurance
  • Bachelor's degree in Computer Science, Engineering, or equivalent experience

Responsibilities

  • Design and operate secure, scalable AI platforms on AWS
  • Develop generative AI solutions using Amazon Bedrock
  • Build containerized AI services on Amazon EKS
  • Develop serverless AI workflows with AWS Lambda and API Gateway
  • Implement secure networking and access controls using AWS services
  • Integrate data services to enhance AI pipelines
  • Establish operational controls aligned with regulatory standards
  • Collaborate with cross-functional teams to ensure compliance

Benefits

  • Opportunity to work with cutting-edge AI technologies
  • Collaborative environment with expert teams
  • Focus on highly regulated, impactful industries
  • Chance to influence AI technology in enterprise settings
  • Flexible work arrangements
  • Continuous learning and professional development opportunities
Full Job Description
JOB SUMMARY
Design, build, and operate secure, scalable AI platforms on AWS for enterprise and regulated environments. Develop and manage generative AI solutions using Amazon Bedrock for model integration and deployment. Build and operate containerized AI services on Amazon EKS supporting Python-based inference and orchestration workloads. Develop serverless AI workflows using AWS Lambda, API Gateway, and event-driven architectures. Implement secure networking and access controls using VPC, IAM, and private endpoints. Integrate data services such as S3, DynamoDB, and messaging/streaming platforms to enable AI pipelines and RAG architectures. Establish CI/CD, monitoring, logging, and operational controls aligned to enterprise and regulatory standards. Collaborate with security, architecture, and risk teams to ensure compliance with financial services requirements.

Key Responsibilities
Design, build, and operate secure, scalable AI platforms on AWS for enterprise and regulated environments
Develop and manage generative AI solutions using Amazon Bedrock for model integration and deployment
Build and operate containerized AI services on Amazon EKS supporting Python-based inference and orchestration workloads
Develop serverless AI workflows using AWS Lambda, API Gateway, and event-driven architectures
Implement secure networking and access controls using VPC, IAM, and private endpoints
Integrate data services such as S3, DynamoDB, and messaging/streaming platforms to enable AI pipelines and RAG architectures
Establish CI/CD, monitoring, logging, and operational controls aligned to enterprise and regulatory standards
Collaborate with security, architecture, and risk teams to ensure compliance with financial services requirements

Required Qualifications
Minimum 5+ years of experience building cloud platforms on AWS
Minimum 2+ years of experience hands-on experience with Amazon Bedrock, EKS/Kubernetes, AWS Lambda, and core AWS networking services
Minimum 8+ years of experience with Python development experience supporting APIs, AI services, or automation workflows
Minimum 5+ years of experience designing secure, highly available, and scalable distributed systems
Minimum 5+ years of experience working in regulated environments such as financial services, banking, or insurance

Degree
Bachelor's degree in Computer Science, Engineering, or equivalent work experience

Preferred Qualifications
Experience supporting AI/ML or generative AI platforms in production
Knowledge of data security, model governance, auditability, and access controls
Experience with Infrastructure as Code (Terraform, CloudFormation, or CDK)
Exposure to CI/CD pipelines and production operations in enterprise environments

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