Description

• Design and develop scalable full stack applications with Angular frontends and microservices-based backends
• Build performant, secure RESTful and GraphQL APIs using modern backend frameworks (Java/Spring Boot, Python/FastAPI)
• Develop responsive, accessible frontends using Angular and TypeScript
• Collaborate with data engineers, security teams, and business analysts to translate regulatory requirements into technical solutions
• Responsibly adopt and leverage AI-assisted development tools (AWS Kiro or others) while maintaining code quality standards and information security hygiene

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CI/CD & Infrastructure

• Design and maintain CI/CD pipelines using tools such as Jenkins and Gitlab
• Implement infrastructure-as-code and containerized deployments for AWS services like Fargate and Lambda
• Integrate automated testing (unit, integration, E2E) and security principles into delivery pipelines

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General Engineering Skills

• Lead code reviews, establish best practices, and contribute to architectural decisions
• Ensure all systems meet technology compliance, audit, cybersecurity, and data governance standards

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Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering, or related field
• 5-7 years of professional software engineering experience
• Strong proficiency in backend languages: Python and/or Java
• 3+ years of production experience with Angular (latest versions), TypeScript, RxJS, and state management (NgRx)
• Experience designing and implementing RESTful APIs and/or GraphQL services
• Hands-on experience with AWS services (Lambda, ECS, API Gateway, S3, RDS, DynamoDB) and containerization (Docker)
• Proficiency with both relational (PostgreSQL) and NoSQL (MongoDB, DynamoDB) databases
• Solid understanding of application security principles (OWASP Top 10, secrets management, least-privilege access)

Preferred Qualifications

• Experience in regulatory or financial services environment
• Experience building human-in-the-loop review systems, annotation platforms, or approval workflows for AI outputs
• Familiarity with event-driven architectures and messaging systems (Kafka, AWS SQS/SNS, Kinesis)
• Exposure to observability and log tooling (Splunk, Datadog, Grafana, CloudWatch) including AI/ML model monitoring
• Experience with microservices patterns (circuit breakers, service mesh, distributed tracing)
• Experience with feature flagging, canary deployments, or progressive delivery strategies
• Contributions to open-source projects or technical publications in AI/ML domains

Key Skills

Education

Bachelor's degree