- Lead a portfolio of diverse technology projects and a team of developers with deep experience in large-scale full-stack systems, distributed microservices, and machine learning systems to create solutions that meet regulatory and business needs.
- Develop and maintain scalable full-stack applications using Java or Python, Go, and Javascript.
- Design and implement cloud-native solutions on AWS, leveraging services like Kubernetes and Serverless infrastructure.
- Work with open-source frameworks to accelerate development and optimize performance.
- Ensure high performance, security, and reliability of applications.
- Collaborate with ML engineers and data scientists to enhance ML observability and monitoring capabilities.
- Share your passion for staying on top of tech trends, experimenting with new technologies, participating in internal & external technology communities, and mentoring other engineers.
- Actively contribute to the codebase and evaluate code to maintain technical quality.
- Collaborate with digital product managers to deliver robust cloud-based solutions that drive powerful experiences to help millions achieve financial empowerment.
- Work with CI/CD pipelines, containerization (Docker, Kubernetes), and DevOps practices to improve deployment efficiency.
- Optimize backend performance and build robust APIs to support various business use cases.
Basic Qualifications:
- Bachelor s Degree
- At least 7 years of experience in software development, with a strong focus on backend and full-stack development.
- At least 5 years of expertise in Java or Python and Go, with the ability to design scalable and maintainable codebases.
- At least 5 years of experience in Javascript for frontend development.
- At least 5 years of experience with AWS services such as Lambda, ECS, EKS, S3, DynamoDB, SQS, IAM.
- At least 5 years of experience of open-source frameworks and their practical applications.
- At least 3 years of experience working on ML Observability tools and frameworks.
Preferred Qualifications:
- Experience in monitoring ML models in production and integrating observability tools.
- Knowledge of event-driven architectures and microservices.
- Familiarity with GraphQL and WebSockets.
- Good understanding of CI/CD pipelines, containerization such as Docker, Kubernetes and DevOps practices.
- Strong understanding of data processing pipelines and streaming architectures.
- Strong problem-solving skills, attention to detail, and the ability to handle complex engineering challenges.
- Proven ability to work in agile environments and contribute to high-performance engineering teams.
