AWS Data & AI Platform Engineer

Role: AWS Data & AI Platform Engineer

Location: Charlotte, NC (Hybrid)

Type: Contract

Description:

Job Description – AI Platform Engineer (AWS – Financial Services)

Day to Day job Duties: (what this person will do on a daily/weekly basis)

• 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

Basic Qualifications: (what are the skills required to this job with minimum years of experience on each)

• 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
• Travel: Minimal travel required; ability to support client stakeholders in regulated environments as needed.  3 days per week in client office.
• Degree: Bachelor’s degree in Computer Science, Engineering, or equivalent work experience

Nice to Have; (But not a must)

• 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