Back to SearchSenior Python Engineer (AWS, AI-Assisted SDLC)
Hybrid in Australia: Sydney
Python.Core& 4 others
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We are seeking a Senior Python Engineer (AWS, AI-Assisted SDLC). You will build cloud-native surveillance analytics and monitoring capabilities that surface patterns and anomalies across large datasets. You will develop APIs and microservices on Amazon Web Services (AWS), integrate AI and machine learning models into production and use AI-assisted tools such as GitHub Copilot to speed up delivery.
Responsibilities
- Build cloud-native services in Python to support surveillance analytics and monitoring workflows
- Develop APIs and microservices that process high-volume trading and market data
- Implement data processing and analytics features using SQL and cloud-based data services
- Integrate machine learning models into production services with appropriate controls and monitoring
- Design and deploy solutions on Amazon Web Services (AWS) using serverless and container patterns
- Improve delivery speed and quality using AI-assisted development tools such as GitHub Copilot
- Contribute to CI/CD pipelines, DevOps practices and operational readiness
- Collaborate with existing teams to deliver changes with minimal ramp-up time
Requirements
- Python skill sets building production services for data-intensive applications
- Amazon Web Services (AWS) experience using services such as Lambda, ECS or EKS, S3, Glue, Athena or Redshift
- SQL capability and experience handling large-scale data processing
- API and microservices engineering background in distributed systems
- Machine learning exposure, including integrating analytical models into production environments
- DevOps and CI/CD understanding for automated build, test and release workflows
- AI-assisted development experience using GitHub Copilot or similar tooling
- Problem-solving ability in fast-moving delivery teams with strong quality standards
Nice to have
- Experience in financial markets, trading surveillance or regulatory technology environments
- Kafka or similar real-time streaming technologies
- Cloud-native architecture experience for distributed, event-driven systems
- Background building analytics, monitoring or data intelligence platforms