What are the responsibilities and job description for the AWS Data - AI Platform Engineer position at Apar Technologies?
Responsibilities: 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